Degree in Mathematical Engineering

Objectives

The aim of the module is to equip students with the necessary algebraic tools to provide digital solutions based on artificial intelligence in the most efficient way possible to solve a given problem.

Prerequisites

No prerequisites have been set.

Learning Outcomes

CB1 Students have demonstrated that they possess and understand knowledge in an area of study building on the foundations of general secondary education; this is typically at a level which, whilst drawing on advanced textbooks, also includes some aspects requiring knowledge from the cutting edge of their field of study

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the skills typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE1 Solve abstract and complex problems relating to Artificial Intelligence using mathematical methods, techniques and concepts to design digital solutions.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE18 Solve mathematical problems by applying numerical methods and algorithms to design digital solutions based on artificial intelligence.

Learning outcomes

RA-1. Understands the main basic theorems of linear algebra.

RA-2. Understands matrix calculus from the conceptual perspective provided by vector and affine spaces.

LR-3. Applies knowledge of linear algebra to solve problems that may arise in engineering.

LA-4. Applies basic concepts of linear systems to solve engineering problems

Course content

Systems of linear equations.

Vector spaces.

Classification of endomorphisms.

Diagonalisation of endomorphisms.

Graph theory

Statistics and combinatorics.

Teaching activities

AP1. – Participatory lectures

AP2. Seminars or practical application classes

AP10. Problem-solving

AP4. – Independent study

AP5. Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 30%

SE2.- Final knowledge assessments: 60%

SE3. – Laboratory practical logbook: 10%

Objectives

The aim of the module is to equip students with the statistical tools needed to understand and gain a deeper insight into solving artificial intelligence problems.

Prerequisites

No prerequisites have been set.

Learning Outcomes

CB1 Students must have demonstrated that they possess and understand knowledge in a field of study building on the foundations of general secondary education; this is typically at a level which, whilst drawing on advanced textbooks, also includes certain aspects requiring knowledge from the cutting edge of their field of study

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the skills typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CG3 To understand the social, ethical and professional responsibilities – and, where applicable, civic responsibilities – associated with the work of a digital technologies professional.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and propose solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CE1 Solve abstract and complex problems relating to Artificial Intelligence by using mathematical methods, techniques and concepts to design digital solutions.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

RA-1 Applies the techniques, methods of representation and summarisation, and measures characteristic of Descriptive Statistics and Inferential Statistics.

RA-2. Determine whether a dataset allows a specific hypothesis to be accepted or rejected, and the error involved in doing so.

LA-3. Determine and quantify the degree of association between statistical variables.

Course content

Elements of data analysis

Descriptive statistics: samples and the distribution of sample characteristics.

Probability distributions

Random variables

Statistical inference models. Statistics and their basic properties

Frequentist approach: point estimation, interval estimation and hypothesis testing

Bayesian approach: posterior distribution, credible intervals and Bayesian tests.

Teaching activities

AP1. Participatory lectures

AP2. Seminars or practical application sessions

AP10. Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 30%

SE2.- Final knowledge assessments: 60%

SE3. – Laboratory practical logbook: 10%

Timetable

Click on this link to view the detailed timetable in Excel

Objectives

The aim of the module is to equip students with programming knowledge that will serve as a basic tool for solving problems in computing and artificial intelligence.

Prerequisites

There are no prerequisites.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the skills typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To acquire, independently, new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CT5 Ethical leadership: The ability to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset to ensure their correct execution on a digital platform.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE14 Build human-computer interfaces so that digital systems offer an optimal user experience whilst complying with accessibility standards.

Learning outcomes

Solve problems using a programming language that utilises external data and interacts with a user.

Devises and writes programmes that solve problems using different algorithmic techniques.

Understands the physical and functional structure of a computer.

Uses a microprocessor’s low-level language and solves problems with it.

Understands and evaluates the different types of storage systems and how they affect the performance of a computer system.

Description of the content

Computational thinking.

Algorithms and representation systems.

Data types and expressions.

Data input and output functions.

Flow control structures.

Memory hierarchy.

Performance assessment

Training activities

AP1 – Participatory lectures

AP2. – Seminars or practical application sessions

AP10. Problem-solving

AP11. Project work

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 30%

SE2.- Final knowledge assessments: 60%

SE4. Portfolio: 10%

Objectives

The aim of the module is to equip students with the soft skills necessary for successful entry into the labour market, enabling them to acquire tools for improved conflict resolution, teamwork, communication, public speaking and leadership.

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or vocation in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and problem-solving within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil liabilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT4 Creativity: Being able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CT5 Ethical leadership: Be able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

Learning outcomes

Learn to communicate successfully in professional meetings and conversations.

Write articles, reports and emails effectively.

Plan, prepare and deliver business presentations.

Convey important professional matters to your audience.

Present the results of your work or a professional message to an audience.

Learn techniques to foster collaboration and teamwork, motivating colleagues and guiding the team towards a common goal.

Prevent, identify and resolve interpersonal conflicts in the workplace.

Course content

Effective communication

Effective presentations

Public speaking

Teamwork

Conflict management.

Training activities

AP1 – Interactive lectures

AP2. Seminars or practical sessions

AP3. Case studies

AP14. Oral presentations

AP15. – Debates

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 40%

SE2.- Final knowledge assessments: 60%

Objectives

The aim of the module is to equip students with knowledge of statistics, graphs, logic, sets and number theory, enabling them to solve more complex problems in computing and artificial intelligence.

Course content

Sets.

Logic

Number theory

Functions

Graph theory

Statistics and combinatorics.

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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Continuous assessment

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 40%

SE2.- Final knowledge assessments: 60%

Regular examination

The exam will cover the full syllabus of the module and a pass mark is a score of 5 out of 10.

Supplementary exam

The exam will cover the full syllabus of the module and a mark of 5 out of 10 is required to pass. Marks obtained during the term or in the ordinary assessment will not be taken into account.

Bibliography

Core reading:

1. Epp, Sussana

Discrete Mathematics with Applications (Spanish Edition)

Publisher: Cengage Learning. 2011.

ISBN: 9786074816211

2. Veerarajan, T.

Discrete Mathematics with Graph Theory and Combinatorics

Published by McGraw-Hill Interamericana. 2008.

ISBN: 9701065301

Supplementary:

3.- Nakos, George

Linear Algebra with Applications

Madrid: Thomson, 1999. 1999.

ISBN: 9687529865

Objectives

The aim of the module is to provide students with a basic understanding of artificial intelligence through the design of an appropriate heuristic for a given problem.

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (normally within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy.

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 To understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE1 Solve abstract and complex problems relating to Artificial Intelligence by using mathematical methods, techniques and concepts to design digital solutions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of Artificial Intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands the historical development of Artificial Intelligence.

Identifies the characteristics of an intelligent system

Identifies which type of search (blind/heuristic/adversarial) is most suitable for tackling a specific problem and implements that search mechanism.

Designs an appropriate heuristic for a given problem.

Identify which type of learning (supervised, unsupervised) is most suitable for a given problem and implement the most appropriate learning strategy

Solve problems of varying complexity using artificial intelligence techniques.

Apply advanced artificial intelligence techniques to the design and development of applications.

Course description

Introduction to Artificial Intelligence.

Search Techniques:

Supervised learning:

Unsupervised learning: Semantic networks and frameworks

Surface modelling.

Introduction to Statistical Analysis for Big Data.

Use cases in organisations.

Training activities

AP1.- Interactive lectures

AP2. Seminars or practical application sessions

AP3. Case studies

AP14. Oral presentations

AP15. – Debates

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 20%

SE2.- Final knowledge assessments: 60%

SE3. – Laboratory practical logbook: 20%

Objectives

The aim of the module is to broaden students’ knowledge of statistics so that they can tackle more complex computational problems.

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To acquire, independently, new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: To be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE1 Solve abstract and complex problems relating to Artificial Intelligence by using mathematical methods, techniques and concepts to design digital solutions.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

RA-1 Understands the basic principles of experimental design and regression models.

RA-2 Applies various techniques and models for multivariate data analysis.

RA-3 Manages the elements of quality control.

RA-4 Uses initial time-series analysis and models to solve engineering problems of varying levels of difficulty.

RA-5 Is able to use statistical software and interpret its results.

Course content

Regression techniques and design of experiments.

Multivariate inferential analysis and multivariate techniques.

Process control: quality analysis.

Time series: basic models.

Applications to the field of Artificial Intelligence.

Teaching activities

AP1. – Participatory lectures

AP2. – Seminars or practical application sessions

AP10. Problem-solving

AP13.- Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. – Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 30%

SE2.- Final knowledge assessments: 60%

SE3. – Laboratory practical logbook: 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 To understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE3 Abstract data and models to store the internal representations of artificial intelligence models, such as linear classifiers and deep learning networks.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE16 Process large amounts of data using machine learning and predictive analytics for application in different knowledge contexts.

Learning outcomes

Understands database technology

Learns the theoretical concepts of the relational model

Learns to programme database access routines using the SQL language

Understand the problems and requirements associated with the management, acquisition and storage of large volumes of data.

Develop applications involving the design, implementation and administration of databases

Gain an understanding of the concepts of normalisation, database design and administration, and their integration into information systems.

Course content

Dynamic data structures.

Search and sorting algorithms.

Introduction to algorithm efficiency.

Analysis of algorithm efficiency.

Application of data structures to problem-solving.

Algorithm design.

Learning activities

AP1. – Participatory lectures

AP2. – Seminars or practical application sessions

AP12. – Problem-solving challenges

AP14. Oral presentations

AP4. Independent study

AP5.- Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities requiring the student’s physical or virtual presence will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%

Objectives

To introduce students to Object-Oriented Programming (OOP) using Python. To understand the fundamentals of OOP and apply them when writing programmes.

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the skills typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To acquire, independently, new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creative environments.

CT5 Ethical leadership: Being able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset to ensure their correct execution on a digital platform.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE14 Build human-computer interfaces so that digital systems offer an optimal user experience whilst complying with accessibility standards.

Learning outcomes

Understands the principles of Object-Oriented Programming.

Understands how the elements of object-oriented programming work.

Understands how classes work and how instances can be created from them.

Implement and call methods. Understand their purpose within classes.

Define instance attributes and class attributes. Learn the differences between them.

Work with inheritance to reuse code, improve design and avoid repetition.

Practise key aspects of object-oriented programming.

Apply object-oriented programming in the Python language.

Course description

Introduction to Object-Oriented Programming.

Encapsulation and inheritance.

Polymorphism.

Abstraction.

Exception handling.

Dynamic memory.

Concurrent programming.

Programming for data science.

Training activities

AP1 – Interactive lectures

AP2. Seminars or practical application sessions

AP10. Problem-solving

AP11. Project work

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE4.- Portfolio 10%

Prerequisites

No prerequisites have been set.

Competencies

CB1 Students have demonstrated that they possess and understand knowledge in a field of study building on the foundations of general secondary education, typically at a level which, whilst drawing on advanced textbooks, also includes some aspects requiring knowledge from the cutting edge of their field of study

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to convey information, ideas, problems and solutions to both specialist and non-specialist audiences

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE1 Solve abstract and complex problems relating to Artificial Intelligence by using mathematical methods, techniques and concepts to design digital solutions.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands and implements the various methods for solving linear systems, both direct and iterative.

Handles the various matrix factorisations.

Calculates and plots interpolation polynomials and cubic spline interpolation functions of a real-valued function.

Approximates the value of definite integrals and the roots of a non-linear equation to a specified degree of accuracy, choosing the most appropriate method for the situation.

Course description

Numerical methods for solving non-linear equations. Calculation of roots of polynomials.

Numerical Linear Algebra: QR factorisation. Approximation of the eigenvalues and eigenvectors of a matrix.

Singular value decomposition. Linear least squares. Pseudoinverse of a matrix.

Solving systems of linear equations using direct and iterative methods

Learning activities

AP1. – Participatory lectures

AP2. – Seminars or practical application sessions

AP10. Problem-solving

AP4. – Independent study

AP5. Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%

Objectives

The aim of the module is to equip students with the necessary algebraic tools to provide digital solutions based on artificial intelligence in the most efficient way possible to solve a given problem.

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CT1 Effective communication: To be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE1 Solve abstract and complex problems relating to artificial intelligence by using mathematical methods, techniques and concepts to design digital solutions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

Learning outcomes

Applies knowledge of algorithms and computational complexity to solve problems that may arise in computer science and artificial intelligence.

Identifies and proposes solutions to problems relating to algorithm efficiency

Calculates the efficiency of iterative algorithms by applying the appropriate calculation rules.

Designs and scales algorithms for environments of varying size and complexity

Solves problems that may arise in computer science and artificial intelligence by applying knowledge relating to the structure and programming of computer systems.

Course content

Dynamic data structures.

Search and sorting algorithms.

Introduction to algorithm efficiency.

Analysis of algorithm efficiency.

Application of data structures to problem-solving.

Algorithm design.

Learning activities

AP1. – Participatory lectures

AP2. Seminars or practical sessions

AP10. Problem-solving

AP11. Project work

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be specified in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.): 30%

SE2.- Final knowledge assessments: 60%

SE3. – Laboratory practical logbook: 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CT1 Effective communication: To be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: The ability to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE6 Develop centralised or distributed computer systems or architectures by integrating hardware, software and networks to build digital solutions based on artificial intelligence.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

Learning outcomes

RA-1 Design of sequential and combinational digital circuits.

LR-2 Production of reports on the design, implementation and testing of low-level programmes and their laboratory testing.

RA-3 Preparing reports on the hardware configuration of computer systems that meet specific criteria.

LA-4 Understanding the evaluation and performance characteristics of hardware and their application to computer systems.

LA-5 Knowledge of new hardware components and storage systems.

RA-7 Understanding of concepts relating to the structure and operation of operating systems.

RA-8 Designing processes that utilise operating system services.

Course content

Introduction to computer architecture.

Instructions and addressing modes.

Control unit and data path.

Memory and input/output in the microprocessor

Introduction to operating systems.

Processes and threads.

Processor scheduling.

Communication and synchronisation.

Memory management.

File management and I/O

Training activities

AP1. – Interactive lectures

AP2. Seminars or practical application sessions

AP10. Problem-solving

AP13.- Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Continuous assessment is subject to attendance at 70 per cent of classes. Should this condition not be met, the student will forfeit all marks for continuous assessment and must sit the full course in the ordinary examination.

Objectives

Through the course’s four teaching units, the aim is to develop the following

skills and learning outcomes:

▪ Core competences:

Understand the data-driven business philosophy

Understand the requirements underlying data science work.

Be able to apply problem-solving skills.

▪ Specific skills:

Master the Python pandas library.

Be agile and proficient in formulating and executing SQL queries

Be familiar with the most common techniques for exploratory data analysis and

variables

▪ Learning outcomes:

Be able to work with the various systems for acquiring, transforming and

writing

Be able to use exploratory analysis techniques to generate an

analytical report that can be delivered to the business

Course content

The work plan presented here is the predefined plan for successfully completing the module

.

• The module consists of 4 teaching units:

o Unit 1 (1.5 ECTS): Introduction to data science

o Unit 2 (2 ECTS): Working with ETL

o Unit 3 (1.5 ECTS): Exploratory analysis of variables

o Unit 4 (1 ECTS): Variable engineering

• The module comprises two learning activities, corresponding to units

1–2 and units 3–4.

• This module begins on 8 September 2025 and ends on 18

December 2024.

• The final examination for the course/module must be taken on the date specified in the

campus

Assessment system and criteria

The final mark (x) will be calculated using the following formula:

x = 0.5 · x_con + 0.5 · x_ord

Where:

x_ord is the mark, on a scale of 0 to 10, obtained in the ordinary assessment period (either through two mid-term exams or through the final exam).

x_con is calculated as:

x_con = (3·x_1 + 2·x_2) / 5

where x_1 and x_2 are the marks, on a scale of 0 to 10, obtained for the two learning activities corresponding to units 1–2 and 3–4. The first activity carries greater weight than the second, as it covers more content.

The above formula will only apply if both x_con and x_ord are equal to or greater than 5.

In summary:

50 per cent of the final mark corresponds to the assessment of coursework.

The remaining 50 per cent is based on assessment via two mid-term exams or, alternatively, via the final exam.

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT5 Ethical leadership: Be able to motivate, influence and lead others, fostering their development so that they collaborate effectively and achieve common objectives within a framework of values.

CE4 Manage projects based on digital technologies, drawing up task plans, monitoring activities, controlling the budget and ensuring that objectives are met within the established deadlines and to the required quality standards.

CE6 Develop centralised or distributed IT systems or architectures by integrating hardware, software and networks to build digital solutions based on artificial intelligence.

CE7 Develop cloud computing systems using microservices so that end users can easily create and run applications on remote servers.

CE12 Use agile software development methodologies with state-of-the-art machine learning development tools.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

Learning outcomes

Learn the general concepts of cloud computing

Gain an understanding of the fundamental systems on which the cloud is based.

Manage the requirements of a cloud computing system.

Become familiar with the techniques and tools used in continuous application integration models.

Learn the fundamentals of Amazon Web Services (AWS)

Develop practical skills using the core Amazon Web Services (AWS) services

Build your knowledge from beginner level through to advanced concepts.

Understand how to get started with Azure

Create virtual machines

Work with storage options such as BLOB and SQL Server

Basic understanding of services such as Azure Functions, Azure Web Apps, etc.

Course description

Basic Cloud Concepts.

Introduction to Cloud Application Architectures.

Cloud working environments.

AWS technology.

MS-Azure technology.

Google Cloud Technology.

Application lifecycle management.

DevOps and continuous integration fundamentals.

Security and protection services.

Training activities

AP1. Interactive lectures

AP2.- Seminars or practical sessions

AP12. – Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study.

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To acquire, independently, new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creative environments.

CT5 Ethical leadership: Being able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset to ensure their correct execution on a digital platform.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE9 Define and develop the processes and procedures involved in the programming and creation of applications or software designed for web-based devices.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE14 Develop human-computer interfaces so that digital systems offer an optimal user experience whilst complying with accessibility standards.

Learning outcomes

You will learn to build modern websites using application design frameworks and development platforms, as well as high-performance user interface libraries.

You will learn to build responsive web applications using development frameworks specifically designed for mobile devices.

Learn HTML5, CSS3 and JavaScript.

Learn to build websites by applying standards that ensure the interoperability of web pages across different browsers.

Develop front-end web applications that respond quickly to any user interaction and enable persistent data storage.

Develop server-side web applications.

Build a complete website.

Course description

Introduction to continuous integration and continuous development.

Front-end development fundamentals: HTML, CSS, JS, jQuery, JSON.

Working with specific frameworks such as Bootstrap, Angular and React.

Training activities

AP1. – Interactive lectures

AP2. Seminars or practical application sessions

AP11. Project work

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be specified in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE4.- Portfolio 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE12 Use agile software development methodologies with state-of-the-art machine learning development tools.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Learners will learn to correctly apply machine learning techniques to obtain reliable and meaningful results.

Understand the most representative and up-to-date techniques in unsupervised, semi-supervised and supervised learning, with and without reinforcement.

Understand deep learning techniques.

Identify the appropriate data analysis techniques depending on the problem.

Use the latest tools and working environments in the field of machine learning.

Course description

Introduction to machine learning.

Pattern recognition.

Supervised learning.

Unsupervised learning.

Reinforcement learning.

Training activities

AP1. – Interactive lectures

AP2. Seminars or practical application sessions

AP12. – Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creative environments.

CT5 Ethical leadership: The ability to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE4 Manage projects based on digital technologies, drawing up task plans, monitoring activities, controlling the budget and ensuring that objectives are met within the established deadlines and to the required quality standards.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE12 Use agile software development methodologies with the most cutting-edge machine learning development tools.

CE14 Build human-computer interfaces so that digital systems offer an optimal user experience whilst complying with accessibility standards.

Learning outcomes

Understands the phases and methodologies of the digital product creation process.

Create product prototypes to minimise deviations during development.

Understands user-centred design techniques and applies style guides.

Integrate digital design and software development into the creation of digital products.

Apply the principles of usability and accessibility to the design of digital products.

Learn about the various advanced user interfaces for digital products.

Create digital designs tailored to different technology platforms.

Work as part of a team to design a digital user interface project based on a real-world scenario.

Course description

Introduction to digital product design.

Fundamentals of user experience research.

Accessibility.

Information architecture.

Methodologies for carrying out user experience projects.

Design and usability.

Design for voice interfaces.

Design and experience with Augmented Reality.

Design and experience with Virtual Reality.

Design and experience with Augmented Reality.

Design and experience with Virtual Reality.

Training activities

AP1. – Interactive lectures

AP2. Seminars or practical application sessions

AP3. Case studies

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To acquire, independently, new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CT5 Ethical leadership: Being able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset to ensure their correct execution on a digital platform.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE9 Define and develop the processes and procedures involved in the programming and creation of applications or software designed for web-based devices.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE14 Develop human-computer interfaces so that digital systems offer an optimal user experience whilst complying with accessibility standards.

Learning outcomes

Become familiar with web service programming environments.

Design and develop APIs that perform persistent operations.

Develop authentication and authorisation mechanisms for back-end applications

Programme web applications that interact with databases

Incorporate security best practices into web development.

Deploy back-end applications in a production-ready state on Amazon Web Services

Write clean, maintainable code in line with industry standards

Implement efficient logging in a back-end application

Manage web application configuration based on the environment and environment variables

Implement data validation

Course description

Microservices architectures (Hexagonal, CQRS, EDA, etc.),

RESTful API development using open-source tools such as Python, Swagger and OAuth

Integration with SQL and NoSQL databases.

Training activities

AP1. – Interactive lectures

AP2. – Seminars or practical application sessions

AP11. Project work

AP13.- Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be specified in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE4.- Portfolio 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset to ensure their correct execution on a digital platform.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE14 Build human-computer interfaces so that digital systems offer an optimal user experience whilst complying with accessibility standards.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Develops reading comprehension skills enabling students to function effectively in a professional context in English.

Develops listening comprehension enabling them to function effectively in a professional context in English.

Possesses oral communication skills enabling them to function effectively in a professional context in English.

Possess the written communication skills necessary to function effectively in a professional context in English.

Is familiar with vocabulary related to computer science and artificial intelligence.

Course content

Scientific and professional vocabulary relating to digital technologies

Grammar (intermediate level)

Communication with clients.

Training activities

AP1.- Interactive lectures

AP2. Seminars or practical application classes

AP14. Oral presentations

AP15. – Debates

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, attendance at fewer than 70 per cent of the course’s teaching activities requiring the student’s physical or virtual presence will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%

Bibliography

Core:

1. Hill, David

English for Information Technology Level 2 Course Book

Pearson Education. 2012.

ISBN: 9781408269909

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and problem-solving within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset to ensure their correct execution on a digital platform.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE14 Build human-computer interfaces so that digital systems offer an optimal user experience whilst complying with accessibility standards.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands different techniques for creating data visualisations.

Understands different methods for the design, visual coding and interaction with data.

Understands the current state of the art in data visualisation.

Is able to communicate patterns found in data clearly and effectively.

Use tools that enable the creation of data visualisations.

Use tools to create interactive visualisations in a web environment.

Recognise the stages involved in a data visualisation project using any specific software tool.

Understands and proposes alternative ways of visualising the same dataset.

Course content

Data types and data sources

Visualisation of ordinal and numerical data.

Visualisation of multivariate data: scatter plots, Chernoff faces.

Visualisation of structured data: graphs and network representations.

Visualisation of unstructured data: text, data streams, etc.

Visualisation tools for dynamic data

Training activities

AP1.- Participatory lectures

AP2. Seminars or practical application sessions

AP3. Case studies

AP13.- Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. – Knowledge assessments

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE12 Use agile software development methodologies with state-of-the-art machine learning development tools.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Demonstrates an in-depth understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.)

Develops the ability to use machine learning to solve complex problems in various industrial contexts.

Gain practical experience in implementing data science models on datasets.

Learns to implement machine learning algorithms using Python.

In-depth understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.).

Develop the ability to solve complex problems using machine learning in various industrial contexts.

Gain practical experience in implementing data science models on datasets.

Learn to implement machine learning algorithms using Python

Course description

Supervised learning algorithms.

Linear regression

Logistic regression

Unsupervised learning algorithms.

K-means clustering.

KNN.

Reinforcement learning algorithms.

Q-Learning

Applications of machine learning in industry.

Supervised learning algorithms.

Linear regression

Logistic regression

Unsupervised learning algorithms.

K-means clustering.

KNN.

Reinforcement learning algorithms.

Q-Learning

Machine-learning applications in industry.

Teaching activities

AP1. – Interactive lectures

AP2. – Seminars or practical application sessions

AP11. Project work

AP12. – Solving challenges

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT5 Ethical leadership: Be able to motivate, influence and lead others, fostering their development so that they collaborate effectively and achieve common goals within a framework of values.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE6 Develop centralised or distributed computing systems or architectures by integrating hardware, software and networks to build digital solutions based on artificial intelligence.

CE7 Develop cloud computing systems using microservices so that end users can easily create and run applications on remote servers.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE11 Apply predictive models and use natural language processing when working with large datasets.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands the landscape of big data, including real-world examples.

Identifies big data problems and is able to propose solutions using data science.

Understands the architecture and programming models used for big data analysis.

Identify common data processing techniques used in big data analysis

Apply techniques for handling streaming data.

Identify when a big data problem requires data integration

Understand the fundamentals of data extraction and analysis, and their relationship with other disciplines.

Understand classification, association and dependency techniques for knowledge extraction.

Understands techniques for analysing complex data of various types.

Understand the basic concepts of distributed computing and recognise when to apply them.

Understand the basic concepts of edge computing and recognise when to apply them.

Course description

Fundamentals of Big Data.

Hadoop and Spark architectures (Datasets, DataFrames, Pyspark) working on one of the leading cloud platforms on the market.

Big data modelling.

Preparation and selection of big data.

Integration and processing of big data.

Introduction to Edge Computing.

Training activities

AP1.- Interactive lectures

AP2. Seminars or practical application sessions

AP10. Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities—and, where applicable, civil responsibilities—associated with the work of a digital technologies professional.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE8 Understand issues related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

Learning outcomes

Understands the theory behind the Fourth Industrial Revolution and relates it to humanity’s major technological advances.

Understands exponential digital technologies and their convergence with physical and biological technologies.

Identifies the possibilities of automation and its impact on innovation and productivity within organisations.

Understands new models of digital interaction and communication between businesses and their customers, and the opportunities for disruptive business models.

Identify new approaches to regulation and agile management of public services.

Recognise the challenges to security, ethics and inequality posed by the digital economy.

Understand the theory of the Fourth Industrial Revolution and relate it to humanity’s great technological advances.

Understand exponential digital technologies and their convergence with physical and biological technologies.

Identify the possibilities of automation and its impact on innovation and productivity within organisations.

Understand the new models of interaction and digital communication between companies and their customers, and the opportunities for disruptive businesses.

Identify new approaches to regulation and the agile management of public services.

Recognise the challenges to security, ethics and inequality posed by the digital economy.

Course description

An introduction to the Fourth Industrial Revolution.

Exponential technologies and the convergence of worlds.

Innovation and productivity associated with exponential technologies

Disruptive digital businesses.

Governance and regulation of the digital economy and society.

Defence and security. The internet as a battlefield.

Ethics and inequality in the Fourth Industrial Revolution.

Introduction to the Fourth Industrial Revolution.

Exponential technologies and the convergence of worlds.

Innovation and productivity associated with exponential technologies.

Disruptive digital business.

Governance and regulation of the digital economy and society.

Defence and security. The Internet as a battlefield.

Ethics and inequality in the Fourth Industrial Revolution.

Teaching activities

AP1. – Participatory lectures

AP2. – Seminars or practical application classes

AP3.- Case studies

AP14. Oral presentations

AP15. – Debates

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of professionals in digital technologies.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creative environments.

CT5 Ethical leadership: The ability to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE4 Manage projects based on digital technologies, drawing up task plans, monitoring activities, controlling the budget and ensuring that objectives are achieved within the established deadlines and to the required quality standards.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE12 Use agile software development methodologies with the most cutting-edge machine learning development tools.

Learning outcomes

Recognises and appreciates the importance and necessity of project management.

Uses support tools for project planning and management.

Understands the key responsibilities of a project manager.

Analyses and makes decisions regarding the management and planning of the different phases of a project – such as planning, integration, scope, deadlines, costs, procurement and quality – as conceived for the purposes of this module.

Identifies and analyses the resources, communications and risks involved in the development process of an engineering project in the field of digital technologies and artificial intelligence.

Understand the factors that determine technology management in a business environment.

Understands the phases involved in the implementation and management of R&D&I projects.

Understands and adheres to the quality standards and regulations applicable in the field of digital technologies and artificial intelligence.

Course content

Scrum and Scrum SAFe methodology.

Kanban methodology.

Iterative development and Minimum Viable Product (MVP) using agile methodologies.

Adapting agile methodologies to artificial intelligence.

Change Management.

Training activities

AP1. – Participatory lectures

AP2. Seminars or practical application sessions

AP3. Case studies

AP14. Oral presentations

AP4. Independent study

AP5. Tutorials

AP6. Knowledge tests

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities requiring the student’s physical or virtual presence will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%

Objectives

The Advanced Information Modelling module focuses on the study and application of non-relational databases, with a particular emphasis on MongoDB. In this module, students gain an in-depth understanding of the fundamental concepts and principles of NoSQL databases and acquire practical skills in the design, implementation and management of flexible and scalable data storage systems.

Prerequisites

No prerequisites have been set.

Learning Outcomes

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and problem-solving within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT5 Ethical leadership: Be able to motivate, influence and lead others, fostering their development so that they collaborate effectively and achieve common goals within a framework of values.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE6 Develop centralised or distributed computing systems or architectures by integrating hardware, software and networks to build digital solutions based on artificial intelligence.

CE7 Develop cloud computing systems using microservices so that end users can easily create and run applications on remote servers.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE11 Apply predictive models and use natural language processing when working with large datasets.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Learns to build scalable and reliable data mining processes.

Design database systems suited to each specific problem.

Designs database systems for machine learning.

Monitor data streams and machine learning models.

Design scalable distributed database systems.

Understand the different NoSQL database models.

Understand the characteristics of the main NoSQL databases.

Learn how to manipulate data in the MongoDB document database.

Course description

Distributed databases

High availability of databases.

Database fault tolerance.

High performance and scalability for managing large volumes of data.

Fundamentals of NoSQL databases.

Types of NoSQL databases.

Programming for access to NoSQL databases.

Introduction to MongoDB.

Basic and advanced queries in MongoDB.

Introduction to graph databases.

Training activities

AP1. – Participatory lectures

AP2. – Seminars or practical sessions

AP10. Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. – Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: The ability to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE4 To lead projects based on digital technologies, drawing up task plans, monitoring activities, managing the budget and ensuring that objectives are achieved within the established deadlines and to the required standard.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE6 Develop centralised or distributed IT systems or architectures by integrating hardware, software and networks to build digital solutions based on artificial intelligence.

CE8 Understand issues relating to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner. Understand the fundamentals of cryptography.

Learning outcomes

Understands the fundamentals of cryptography.

Understands the main encryption algorithms.

Understands public-key-based signature and verification systems

Understands security measures for information systems and networks.

Understands identity verification mechanisms in digital systems.

Define security policies for corporate information systems.

Carry out security audits of digital systems and networks

Understand the fundamentals of cryptography.

Be familiar with the main encryption algorithms.

Understand signature and verification systems based on public keys.

Understand the security measures for information systems and networks.

To understand the mechanisms of identity verification in digital systems.

Define security policies for corporate information systems.

Carry out security audits of digital systems and networks

Course description

Introduction to information systems security.

Symmetric and asymmetric cryptography.

Public-key encryption algorithms.

Identity verification systems.

Security in information systems and networks.

Security policies and strategies.

Introduction to information systems security.

Symmetric and asymmetric cryptography.

Public-key encryption algorithms.

Identity verification systems.

Security in information systems and networks.

Security policies and strategies.

Teaching activities

AP1. – Interactive lectures

AP2. Seminars or practical sessions

AP3. Case studies

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creative environments.

CT5 Ethical leadership: Being able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of Artificial Intelligence models such as linear classifiers and deep learning networks.

CE4 Manage projects based on digital technologies, drawing up task plans, monitoring activities, controlling the budget and ensuring that objectives are achieved within the established deadlines and to the required quality standards.

CE6 Develop centralised or distributed IT systems or architectures by integrating hardware, software and networks to build digital solutions based on artificial intelligence.

CE7 Develop cloud computing systems using microservices so that end users can easily create and run applications on remote servers.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE12 Apply agile software development methodologies using state-of-the-art machine learning development tools.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands the appropriate DevOps tools for deploying higher-quality applications.

Understands the DevOps software process and its various environments to improve performance.

Learns to oversee the transformation of applications from on-premises to hybrid and cloud deployments.

Understand how to modernise the management of technology operations using artificial intelligence.

Learn best practices for agile development and continuous delivery.

Learn how to build, test and deploy applications in the cloud using DevOps tools and practices.

Learn the basics of continuous integration, continuous delivery and continuous deployment.

Learn how to install and configure containerised systems.

Run stateless and stateful applications on containerised systems.

Scale your applications using metrics.

Know the appropriate DevOps tools to run applications with higher quality.

Understand the DevOps software process and its various environments to improve performance.

Learn to oversee the transformation of applications from on-premises to hybrid and cloud deployments.

Understand how to modernise technology operations management using artificial intelligence.

Learn best practices for agile development and continuous delivery.

Learn how to build, test and deploy applications in the cloud using DevOps tools and practices.

Learn the basic concepts of continuous integration, continuous delivery and continuous deployment.

Learn to install and configure container systems.

Be able to run stateless and stateful applications on container systems.

Be able to scale your applications using metrics.

Course description

DevOps methodology,

MLOps methodology

Cloud-native fundamentals (microservices, containers),

Real-time architectures (Kafka), working on one of the leading cloud platforms on the market.

DevOps methodology,

MLOps methodology

Cloud-native fundamentals (microservices, containers...),

Real-time architectures (Kafka), working on some of the leading cloud platforms on the market.

Training activities

AP1. – Interactive lectures

AP2.- Seminars or practical application sessions

AP11. Project work

AP12. – Solving challenges

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE4.- Portfolio 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT5 Ethical leadership: Being able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

Learning outcomes

Understands how to incorporate artificial intelligence into a business strategy.

Learns to develop a roadmap for implementing artificial intelligence in a business context.

Identify the organisational implications of integrating robotics, natural language processing and machine learning into business.

Be able to apply key AI management and leadership skills to support informed strategic decision-making.

Gain a practical grounding in artificial intelligence and its business applications to transform organisations into the businesses of the future.

Know how to incorporate artificial intelligence into a business strategy.

Learn to develop a roadmap for implementing artificial intelligence in a business context.

Identify the organisational implications of integrating robotics, natural language processing and machine learning into business.

Know how to leverage key management and leadership insights from AI to support informed strategic decision-making.

Acquire a practical grounding in artificial intelligence and its business applications to transform organisations into enterprises of the future.

Course description

Strategy for implementing artificial intelligence within an organisation.

Machine learning in business

Natural language processing in business

Robotics in business.

Artificial intelligence in business and society

Success stories.

The future of artificial intelligence.

Strategy for implementing artificial intelligence in an organisation.

Machine learning in the enterprise

Natural language processing in business

Robotics in business.

Artificial intelligence in business and society

Success stories.

The future of artificial intelligence.

Training activities

AP1. – Interactive lectures

AP2. – Seminars or practical application sessions

AP3. Case studies

AP14. Oral presentations

AP15. – Debates

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%

Prerequisites

No prerequisites have been set

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG4 To identify risks associated with carrying out projects related to computer science and artificial intelligence.

CG6 Integrate ethical values and an awareness of social, economic and environmental transformation into their professional and scientific practice in the field of computing and information and communication technologies.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and propose solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE12 Use agile software development methodologies with state-of-the-art machine learning development tools.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE16 Process large amounts of data using machine learning and predictive analytics for application in different knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands how deep learning works.

Understands the mathematical foundations of neural networks and their different architectures

Understands the mathematical models of convolutional neural networks.

Understands optimisation techniques for convolutional neural networks.

Understands the concepts and algorithms of reinforcement learning.

Be familiar with the different types of problems to which neural networks can be applied.

Design solutions based on specific neural network architectures applied to complex problems across different industries.

Design, programme, train and run a neural network model.

Course content

Fundamentals of convolutional neural networks.

The multilayer perceptron.

Implementation of a convolutional neural network.

Programming applications that implement convolutional neural networks.

Learning activities

AP1.- Interactive lectures

AP2.- Seminars or practical application classes

AP11. Project work

AP12. – Solving challenges

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE12 Use agile software development methodologies with state-of-the-art machine learning development tools.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands the processes involved in describing a digital image.

Applies image segmentation and pattern recognition techniques.

Applies machine learning and deep learning models to digital image processing.

Designs solutions based on machine learning and deep learning applied to complex problems in image processing and computer vision.

Design, programme, train and run a computer vision model using programming languages and development environments specific to image processing.

Know the processes for describing a digital image.

Apply image segmentation and pattern recognition techniques.

Apply deep learning and machine learning models to digital image processing.

Design solutions based on machine and deep learning applied to complex image processing and computer vision problems.

Design, programme, train and run a computer vision model using programming languages and specific development environments for image processing.

Course description

Introduction to computer vision.

Image segmentation.

Pattern recognition.

Models for representing and describing images.

Image classifiers.

Decision trees

Support Vector Machine (SVM) classifier.

Applications of computer vision.

Introduction to computer vision.

Image segmentation.

Pattern recognition.

Image representation and description models.

Image classifiers.

Decision trees.

Support Vector Machine (SVM) classifier.

Artificial vision applications

Teaching activities

AP1. – Participatory lectures

AP2. Seminars or practical application sessions

AP12. – Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 Abstract data and models to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE11 Apply predictive models and utilise natural language processing when working with large datasets.

CE12 Use agile software development methodologies with the most cutting-edge machine learning development tools.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Apply neural networks to Natural Language Processing, ranging from simple to complex neural models.

Understands the concept of word embeddings and their applications in Natural Language Processing.

Understands recurrent neural networks and LSTM models for analysing texts and generating text summaries.

Understands the transformer network model for analysing the relationships between words in a text.

Design solutions based on specific neural network architectures applied to complex Natural Language Processing problems.

Design, programme, train and run a Natural Language Processing model

Apply neural networks to Natural Language Processing, ranging from simple to complex neural models.

Understand the concept of word embeddings and their applications in Natural Language Processing.

Understand recurrent neural networks and LSTM models for text analysis and synthesis.

Understand the transformational network model to analyse the relationships between words in a text.

Design solutions based on specific neural network architectures applied to complex Natural Language Processing problems.

Design, programme, train and run a Natural Language Processing model.

Course description

Introduction.

Levels of processing (phonetic, morphological, syntactic, semantic, discursive, pragmatic) and their treatment.

Definition and construction of linguistic corpora.

Application of machine learning and deep learning techniques to NLP.

Use cases: pattern recognition, information discovery in texts, sentiment analysis, chatbots.

Introduction.

Levels of processing (phonetic, morphological, syntactic, semantic, discursive, pragmatic) and their treatment.

Definition and construction of linguistic corpora.

Use of machine learning and deep learning techniques in PLN.

Use cases: pattern recognition, information discovery in texts, sentiment analysis, chatbots.

Training activities

AP1. – Participatory lectures

AP2.- Seminars or practical application classes

AP11. Project work

AP12. – Solving challenges

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be specified in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2.- Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

Students must demonstrate a B-2 level of English in accordance with the Common European Framework of Reference for Languages before enrolling on this module

Competences

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT5 Ethical leadership: Being able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE5 Design the accessibility, ergonomics, usability and security of IT systems, applications and services, as well as the information they provide, in accordance with current legislation and regulations.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

Learning outcomes

Understands the ethical issues in the field of artificial intelligence.

Understands national and international legislation addressing ethical issues in artificial intelligence.

Understands and applies legislation on the protection of personal data and privacy.

Demonstrates a proactive approach to incorporating sound ethical practices into the development of artificial intelligence-based solutions.

Resolves ethical problems in the field of artificial intelligence, seeking to prevent negative impacts on the public, particularly on the most disadvantaged groups.

Apply prudence in the design of artificial intelligence-based systems, taking into account very strict prerequisites to avoid potentially harmful outcomes.

Understands and applies techniques to avoid bias in the training of artificial intelligence-based models and algorithms.

Understand the ethical issues in the field of artificial intelligence.

Be familiar with national and international legislation relating to ethical issues in artificial intelligence.

Understand and apply legislation on personal data protection and privacy.

Develop a commitment and take the initiative to incorporate sound ethical practices into the development of artificial intelligence-based solutions.

Solve ethical problems in the field of artificial intelligence, seeking to prevent negative impacts on citizens, particularly the most disadvantaged groups.

Apply prudent criteria when designing systems based on artificial intelligence, taking into account very strict prerequisites to avoid potentially harmful outcomes.

Understand and apply techniques to avoid bias in the training of artificial intelligence models and algorithms

Course content

European legal framework on artificial intelligence.

Regulations on the protection of personal data.

Reliability of artificial intelligence systems.

Accountability of artificial intelligence systems.

Liability for artificial intelligence systems.

Limited autonomy of artificial intelligence systems.

The role of humans in artificial intelligence systems

Case study: the self-driving car.

European legal framework on artificial intelligence.

Regulation on the protection of personal data.

Reliability of artificial intelligence systems.

Accountability of artificial intelligence systems.

Liability of artificial intelligence systems.

Limited autonomy of artificial intelligence systems.

The role of humans in artificial intelligence systems.

Case study: the self-driving car.

Teaching activities

AP1. – Participatory lectures

AP2. – Seminars or practical application sessions

AP3. Case studies

AP14. Oral presentations

AP15. – Debates

AP4. Independent study

AP5. Tutorials

AP6. Knowledge tests

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%

Prerequisites

The masterclasses will consist of practical sessions designed to help students develop communication and teamwork skills and familiarise themselves with professional working environments.

Competencies

CB2 Students should be able to apply their knowledge to their work or vocation in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to form judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil liabilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT3 Analytical thinking: Being able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creative environments.

CT5 Ethical leadership: Being able to motivate, influence and lead others by fostering their development, so that they collaborate effectively and achieve common objectives within a framework of values.

CE21 Apply the knowledge acquired in a work environment to manage tasks with a sense of responsibility and to work as part of a team with initiative and motivation.

Learning outcomes

RA-1. The student should have the ability to organise, plan and manage their time effectively.

LR-2. Students should be able to prepare projects and reports, both orally and in writing, in a business context.

LR-3. Students should possess the capacity for learning, flexibility and the ability to adapt to the professional environment.

LR-4. Students should have the ability to make decisions and solve problems with initiative, autonomy and creativity.

LR-5. Students should be able to demonstrate an ethical commitment and awareness of social, economic and environmental issues.

RA-6. Students should possess leadership skills and the ability to work as part of a team in high-pressure environments.

RA-7. Students should develop interpersonal skills.

RA-8. Students should be able to think strategically and in a results-oriented manner.

RA-9. Students should have the capacity for self-awareness and personal growth within the professional environment.

RA-10. Students should demonstrate entrepreneurial initiative and develop the ability to recognise social problems in diverse contexts and multicultural settings.

Description of the content

The content of the external work placement to be undertaken by the student will be based on work experience at a centre that is already linked to the University through an agreement which expressly sets out the external work placement activities to be carried out at that centre. The chosen topic will be finalised before the student’s placement begins and may relate to various professional aspects.

Training activities

AP1. Participatory lectures

AP4. Independent study

AP5. Tutoring

AP9. External work placements

Assessment system and criteria

SE.7 Report from the external placement tutor 45%

SE.8 Report by the academic supervisor of the external work placement 40%

SE.9 External placement report 15%

Prerequisites

The final-year project defence may be conducted via videoconference provided that:

1) A representative of the university is present in person to verify the student’s identity at the

venue where the student is defending their final-year project and remains with them throughout the defence;

2) The defence is open to the public, either where the student is present or where the examination board is present;

3) There is scope for interaction between the student and the examination board.

Lectures will consist of practical sessions to familiarise students with project-based working methods, the identification of sources and information-searching techniques.

Learning Outcomes

CB1 Students have demonstrated that they possess and understand knowledge in an area of study building on the foundations of general secondary education; this is typically at a level which, whilst drawing on advanced textbooks, also includes certain aspects requiring knowledge from the cutting edge of their field of study

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the skills typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To learn independently new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CG5 Plan tasks for computer science or ICT projects to ensure that established objectives and deadlines are met

CG6 Integrate ethical values and sensitivity towards social, economic and environmental transformation into their professional and scientific practice in the field of computing and information and communication technologies.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE20 Present an original piece of work in the field of Computing and Artificial Intelligence that synthesises the knowledge and skills acquired during the degree programme.

Learning outcomes

RA-1. The student shall individually undertake, present and defend before a university examination board a professional-level project in the field of Business Intelligence that synthesises and integrates the competences required by the programme

Description of the content

The Final-Year Project must demonstrate the student’s acquisition of the general and specific competences of the degree programme, synthesising the competences acquired throughout the course, or through an innovative project in one of the programme’s areas of expertise, of sufficient complexity, in an environment as close as possible to real-world conditions. The student must produce a coherent piece of work, of a realistic duration in relation to the intended objectives. The aim is to produce an original piece of work, through the student’s own research and personal contribution.

Teaching activities

AP1 Participatory lecture

AP5 Tutorial

AP7 Preparation of the Final Year Project

AP8 Public oral defence of the final-year project

Assessment system and criteria

SE 5 Final Year Project Report 70%

AS 6 Defence and presentation of the Final Year Project before the Assessment Panel 30%

Prerequisites

No prerequisites have been set.

Competencies

CB1 Students have demonstrated that they possess and understand knowledge in a field of study building on the foundations of general secondary education, typically at a level which, whilst drawing on advanced textbooks, also includes some aspects requiring knowledge from the cutting edge of their field of study

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the formulation and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CE1 Solve abstract and complex problems relating to Artificial Intelligence using mathematical methods, techniques and concepts to design digital solutions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 To abstract data and models in order to store the internal representations of Artificial Intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands the basic concepts of molecular and cellular biology.

Understands the Human Genome Project, its usefulness and its future potential.

Understands the current challenges in biology that can be addressed using computing technologies and artificial intelligence.

Understands the contribution of data science tools to the Human Genome Project.

Understand the context of digital medicine and the technologies that are ushering in a new era in medicine.

Understand the challenges and opportunities presented by artificial intelligence for improving medicine and healthcare services.

Understand the application of data science in digital medicine.

Knowledge of the basic concepts of molecular and cellular biology.

Be familiar with the Human Genome Project, its usefulness and its potential for the future.

Understand the current challenges in biology that can be addressed using computing and artificial intelligence technologies.

Understand the contribution of data science tools to the Genome Project.

Understand the context of digital medicine and the technologies that are ushering in a new era in medicine.

Understand the challenges and opportunities presented by artificial intelligence for improving medicine and healthcare services.

RA-9. Understand the application of data science in digital medicine.

Course description

Introduction to cellular and molecular biology.

The Human Genome Project.

Genetic engineering.

Bioinformatics and the simulation of biological processes.

Big Data analysis and systems biology.

Introduction to digital medicine.

Digital medicine technologies.

Data science for medicine.

Application of artificial intelligence in disease diagnosis and patient care.

Introduction to cellular and molecular biology.

The Human Genome Project.

Genetic engineering.

Bioinformatics and the simulation of biological processes.

Big Data analysis and systems biology.

Introduction to digital medicine.

Digital medicine technologies.

Data science for medicine.

Application of artificial intelligence in disease diagnosis and patient care.

Teaching activities

AP1. – Participatory lectures

AP2. Seminars or practical sessions

AP3. Case studies

AP12. – Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To acquire, independently, new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil responsibilities – associated with the work of a digital technologies professional.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT3 Analytical thinking: Be able to analyse and evaluate information, break down complex problems or situations, and identify patterns in order to propose solutions and make decisions.

CT4 Creativity: Be able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

Learning outcomes

Understands the fundamentals of blockchain technology

Understands what Bitcoin is and how it works.

Is familiar with applications and use cases of blockchain technology in the financial services sector

Learns about the different categories of crypto-assets and the ways in which transactions can be carried out using blockchain technology.

Learn how blockchain is transforming the economy and society as a whole.

Understand the relationship between blockchain technology and Bitcoin, and its significance.

Consider innovative models for applying blockchain technology.

Acquire the skills to design and implement smart contracts.

Discover methods for developing decentralised applications using blockchain technology.

Learn about blockchain frameworks specific to the financial sector.

Learn about regulation and the fundamental role of data and security in the Fintech industry.

Course description

Blockchain fundamentals.

Blockchain technology platforms.

Smart contracts.

Digital tokens.

Decentralised applications (Dapps)

Cryptography and hash functions.

Introduction to cryptocurrencies and Bitcoin.

How Bitcoin works and the role of blockchain technology.

Bitcoin mining.

Transformations in financial services through blockchain technology and Bitcoin.

Regulation in FinTech.

Barriers and challenges facing Bitcoin technology.

Training activities

AP1. – Interactive lectures

AP2. Seminars or practical application sessions

AP3. Case studies

AP12. – Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be specified in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB3 Students should be able to gather and interpret relevant data (usually within their field of study) in order to make judgements that include reflection on relevant social, scientific or ethical issues

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE3 To abstract data and models in order to store the internal representations of artificial intelligence models such as linear classifiers and deep learning networks.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE10 Process heterogeneous data to extract information and support decision-making through the use of computing and artificial intelligence.

CE11 Apply predictive models and utilise natural language processing when working with large datasets.

CE12 Use agile software development methodologies with state-of-the-art machine learning development tools.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE16 Process large amounts of data using machine learning and predictive analytics for application in various knowledge contexts.

CE19 Understand statistical models using digital and graphical tools to describe different characteristics of interest in their variables and interpret the results.

Learning outcomes

Understands the physical fundamentals of quantum computing.

Understands the challenges of quantum computing that cannot be solved using classical computing.

Understands the mathematical models used in quantum computing.

Understands the main algorithms used in quantum computing.

Understand the architectures, compilers and programming languages for quantum processors.

Use the technology platforms and frameworks that harness quantum computing.

Learn about the current applications of quantum computing-based solutions and their future potential.

Course description

Introduction to quantum computing.

Mathematical models. The qubit.

Grover’s algorithm.

Shor’s factorisation algorithm.

Technological platforms for quantum computing.

Practical applications.

Training activities

AP1. – Interactive lectures

AP2. Seminars or practical application sessions

AP10. Problem-solving

AP11. Project development

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set.

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CE1 Solve abstract and complex problems relating to Artificial Intelligence using mathematical methods, techniques and concepts to design digital solutions.

CE2 Design algorithms to solve a specific problem by using an appropriate programming language and preparing a dataset for their correct execution on a digital platform.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

CE13 Write computer programmes using modern programming languages for artificial intelligence to develop prototypes of digital solutions.

CE15 Build mechanical devices or robots capable of performing tasks in response to commands from humans.

CE17 Solve mathematical problems by applying techniques from graph theory and algorithms.

Learning outcomes

Understands process mining and its relationship with data processing.

Understands the basic concepts of robotic process automation (RPA).

Distinguishes RPA from traditional automation

Understands how RPA will affect business processes within an organisation.

Understands the different RPA architectures for building solutions by creating software robots that automate repetitive tasks.

Understand and carry out a feasibility and complexity analysis of the identified RPA candidates

Identify which types of processes within an organisation are best suited for automation using RPA.

Course content

Fundamentals of process re-engineering.

Lean methodology.

Process mining.

Robotic Process Automation (RPA).

RPA tools.

Application of machine learning to process automation (Cognitive Automation).

Training activities

AP1.- Interactive lectures

AP2. – Seminars or practical sessions

AP3. Case studies

AP12. – Problem-solving

AP13. Workshop and/or laboratory activities

AP4. Independent study

AP5. – Tutoring

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

----

SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 30%

SE2. Final knowledge assessments 60%

SE3.- Laboratory practical logbook 10%

Prerequisites

No prerequisites have been set

Competencies

CB2 Students should be able to apply their knowledge to their work or profession in a professional manner and possess the competences typically demonstrated through the development and defence of arguments and the resolution of problems within their field of study

CB4 Students should be able to communicate information, ideas, problems and solutions to both specialist and non-specialist audiences

CB5 Students should have developed the learning skills necessary to undertake further study with a high degree of autonomy

CG1 To acquire, independently, new knowledge and techniques appropriate for the design, development or operation of digital information systems.

CG2 To communicate effectively, both in writing and orally, knowledge, procedures, results and ideas relating to digital technologies and artificial intelligence, whilst being aware of their socio-economic impact.

CG3 Understand the social, ethical and professional responsibilities – and, where applicable, civil liabilities – associated with the work of a digital technologies professional.

CG4 Identify risks associated with carrying out projects related to computer science and artificial intelligence.

CT1 Effective communication: Be able to convey and adapt a message through active listening in order to connect with the audience, using the necessary resources to adapt to their characteristics and those of the context.

CT2 Teamwork: Be able to interact, listen empathetically and offer solutions by integrating different points of view, in order to collaborate effectively with people from different fields and disciplines in achieving common objectives.

CT4 Creativity: Being able to generate innovative ideas and solutions to complex problems or situations in collaborative and co-creation environments.

CT5 Ethical leadership: Be able to motivate, influence and lead others, fostering their development so that they collaborate effectively and achieve common objectives within a framework of values.

CE4 Manage projects based on digital technologies, drawing up task plans, monitoring activities, controlling the budget and ensuring that objectives are met within the established deadlines and to the required standard.

CE8 Understand problems related to computer science and artificial intelligence, in order to apply the best solution efficiently and in a timely manner.

Learning outcomes

Learns to organise, plan and manage time effectively.

Develops decision-making and problem-solving skills with initiative, autonomy and creativity.

Develops an ethical commitment and awareness of social, economic and environmental issues.

Develop interpersonal skills.

Learn to think strategically and in a results-oriented manner.

Gain practical knowledge of the processes involved in setting up a business and understand the specific aspects of digital entrepreneurial management.

Course description

Entrepreneurship in the digital environment.

Historical evolution and trends in digital business models.

Identifying and analysing digital business models.

The entrepreneurial spirit.

Creativity and business ideas.

The business model.

Strategic analysis and objectives.

The company’s marketing, production, organisational and financial plans.

Steps to setting up the company.

Advantages and disadvantages of setting up a digital business.

Training activities

AP1. – Participatory lectures

AP2. Seminars or practical sessions

AP3. Case studies

AP14. Oral presentations

AP15. – Debates

AP4. Independent study

AP5. Tutorials

AP6. Knowledge assessments

Assessment system and criteria

Without prejudice to any other requirements that may be set out in the relevant course syllabus, as a general rule, failure to attend more than 70 per cent of the course’s teaching activities—which require the student’s physical or virtual presence—will result in the loss of the right to continuous assessment during the standard examination period. In this case, the examination to be held during the official period set by the University will be the sole assessment criterion, with the weighting specified in the course syllabus.

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SE1.- Practical activities (case studies, problem-solving and challenges, project work, oral presentations, debates, etc.) 40%

SE2.- Final knowledge assessments 60%