Online Master’s Degree in Artificial Intelligence

Objectives

▪ RK3: Explain the existing rules and regulations in AI environments

▪ RK5: Identify the uses and applications of AI

▪ RK6: Interpret the process of generating an AI model, its phases and its deployment

deployment

▪ RODS: Develops effective communication, teamwork,

, creativity and ethical leadership from a cross-disciplinary perspective and with

clear inspiration drawn from democratic principles and values, as well as the

to operate with integrity in a professional environment

▪ Has a thorough understanding of the various stages involved in managing an automated learning project

and the most common tools required to carry out this task successfully

▪ Implements the legal and regulatory requirements within the scope of an AI project

to ensure that its implementation will not result in compliance issues for the

organisation

▪ Apply the regulations governing the use of AI algorithms

▪ Core competencies:

• Students will learn about the main use cases and specific examples of

the application of AI in the business world, as well as the basic concepts required to

function as a data scientist within an organisation

▪ Specific skills:

• Concepts will be explored relating to the more effective implementation of

analytical and artificial intelligence models in the areas of legislation,

project management, best design practices and behavioural economics, as well as

advanced analytics project management.

Course description

1. Artificial Intelligence in the business world: As an introduction, we will look at

how, first and foremost, the availability of large amounts of data, as well as

the tools for processing it, and the artificial intelligence models

derived from them, have completely revolutionised industries and the world

which we live, and what this has meant for different organisations and

business sectors

2. Data Governance: We will explore and work on, both theoretically and practically, the

key concepts of data governance and its lifecycle within the organisation,

from the perspective of the data user, as well as from the perspective of the data

owner or the person responsible for data quality and availability.

3. BECO and Design: General Principles of Behavioural Economics and Data-Driven Design

and their relevance to the creation and development of analytical projects.

4. Project Management: The project management lifecycle within the organisation.

Concepts of planning, time management, teams and dependencies, and the principles of

the agile philosophy of project development.

5. Ethical and Legal Aspects: An overview of the environments and regulations affecting

Artificial Intelligence models, key legislative initiatives currently underway, and

ethical considerations to bear in mind when developing models.

6. MLOps: integrating AI with operational systems: we will explore in

how to effectively integrate machine learning into the business world,

how to harness the benefits of the cloud to deploy models at scale,

apply DevOps practices for efficient management, and learn from real-world use cases

across various industries. We will also maintain a focus on the

latest trends in machine learning.

Assessment system and criteria

In the course/module’s virtual classroom, you will be able to view in detail the activities you are required to

complete, as well as the submission dates, assessment criteria and marking schemes for each of

them.

Your final mark will be based on the following assessment system:

50 per cent of your mark will be based on your continuous assessment. The following will be taken into account:

Individual and/or group activities: these are included in the continuous assessment.

The final exam for the module will account for: 50% of the final mark.

Bibliography

Essential:

1. Daniel Kahneman

Thinking, Fast and Slow

Penguin Books. 2011.

ISBN: 9780141033570

2. Ken Schwaber & Jeff Sutherland

The Scrum Guide

Scrum.org. 2024.

ISBN: 0000000000

3. Zhamak Dehghani

Data Mesh: Delivering Data-Driven Value at Scale

O’Reilly. 2022.

ISBN: 1492092398

Objectives

Knowledge

- Recognises the most commonly used statistical theories in AI environments.

- Describes the metrics used in the calibration of AI models.

Skills

- Applies the statistics associated with the most common AI algorithms.

- Identifies the algorithm required to tackle an AI problem.

Competencies

- Fairly evaluates different AI-based solutions and selects the most effective one for achieving the stated objectives.

- Assesses the quality of an AI model based on metrics for comparing different algorithms

Course description

Unit 1: Specialisation in statistics for the description and analysis of large datasets.

Unit 2: Estimation theory

Unit 3: Statistical decision-making and classification

Unit 4: Bayesian statistics

Unit 5: Statistical data generation: synthetic datasets and imputation of missing data

Unit 6: Complex graph theory and its applications to AI

Assessment system and criteria

Assessment system

50% of the mark will be based on continuous assessment (two practical assignments, the final mark for which will be the average of both assignments).

50% of the mark will be based on the final examination.

Ordinary examination session

To pass the module in the ordinary assessment period, students must achieve a mark of 5.0 out of 10 or higher in the final mark (weighted average) for the module and, in addition:

The average mark for all activities (practical assignments) must be 5.0 out of 10 or higher to be included in the overall average with the exam. Similarly, the exam mark must be 5.0 out of 10 or higher to be included in the overall average with the activities.

Extraordinary examination session

To pass the module in the resit, students must achieve a final mark of 5.0 out of 10 or higher.

Students must submit any assignments that were not passed during the ordinary assessment period, after receiving the relevant feedback from the lecturer, or any assignments that were not submitted at all.

Addendum

Core competencies

Students will be able to understand the characteristics that distinguish the development of a supervised learning model for explanatory purposes from one for predictive purposes, and in particular the differences in the evaluation metrics used for each.

Specific competences

Students will be able to understand the types of data and applications for which different architectures are appropriate, such as neural networks or graph-based models.

Bibliography

Core:

1. Albert, Réka, and Albert-László Barabási

Statistical Mechanics of Complex Networks

Reviews of Modern Physics 74.1: 47. 2002.

ISBN: 0034-6861

2. Boccaletti, Stefano, et al.

Complex networks: Structure and dynamics

" Physics Reports 424.4–5, pp. 175–30. 2006.

ISBN: 0370-1573

3. Boccaletti, Stefano, et al.

The structure and dynamics of multilayer networks

Physics Reports 544.1, pp. 1–122. 2014.

ISBN: 00000-00000

4. Euler, Leonhard.

Leonhard Euler and the Königsberg bridges

Scientific American. 1953.

ISBN: 0036-8733

5. Gareth, J.; Witten, D.; Hastie, T. and Tibshirani, R

An Introduction to Statistical Learning: With Applications to R

Springer. 2017.

ISBN: 9788074350887

6. Hastie, T.; Tibshirani, R. and Friedman, J.

“The Elements of Statistical Learning: Data Mining, Inference and Prediction (2nd ed.)

2nd ed. Springer. 2017.

ISBN: 9780387848570

7. Newman, Mark EJ.

The Structure and Function of Complex Networks

" SIAM Review 45.2. pp. 167–256. 2003.

ISBN: 0036-1445

8. Partida, Alberto, Regino Criado, and Miguel Romance

Identity and access management: resilience against intentional risk for blockchain-based IoT platforms

Electronics 10.4: 378. 2021.

ISBN: 2079-9292

Objectives

Through the course’s four teaching modules, the aim is to develop the following competences and learning outcomes:

According to the MUIA course description, this module requires the following learning outcomes:

In terms of knowledge or content:

RK1: Understands the most commonly used programming languages, libraries and frameworks in the field of AI.

In terms of skills:

RC3: Has a command of the various stages involved in managing a machine learning project and the most common tools for carrying out this task successfully.

RC5: Adapt the various available technologies and algorithms for use in solving AI problems.

In terms of skills:

RS2: Has a command of the most common libraries and tools in the field of artificial intelligence.

▪ Core competences: students will be able to understand the role played by the libraries and programming environments covered in the module.

▪ Specific competences: students will be able to use the libraries and programming environments covered in the module.

▪ Learning outcomes: Students will produce the following learning outcomes: A Python programme using the libraries studied in Module 2. An R programme using the libraries studied in Modules 4–6. Both programmes will be developed in an IDE.

Course content description

The first module will focus on introducing Python as one of the industry standards, not only for machine learning processes but for the industry in general. We will learn more about the main IDEs used, and we will give a brief overview of SQL as a language for analysing our data.

The second module explains the core Python modules that form the basis of Python programming for artificial intelligence. Examples of these specialised modules include Pandas, NumPy and SciPy. We will also introduce the use of Python visualisation libraries such as Matplotlib and Seaborn.

The third module will introduce two of the main artificial intelligence frameworks: SKLearn, which focuses on traditional models, and TensorFlow, which is more geared towards deep learning. We will conclude with an introduction to version control using Git.

The fourth, fifth and sixth modules follow the same structure as the first two, but this time using R as the programming language, with a particular focus on its native features and libraries for data analysis (dplyr, data.table) and visualisation, such as ggplot2.

Assessment system and criteria

In the course/module’s virtual classroom, you will be able to view in detail the activities you are required to complete, as well as the submission dates, assessment criteria and marking schemes for each one.

Your final mark will be based on the following assessment system:

50% of the mark will be based on the assignments (one in Python and one in R)

The final exam for the course will account for the remaining 50 per cent of your mark.

Bibliography

Essential:

1. Chang, Winston

R Graphics Cookbook

O’Reilly Media. 2013.

ISBN: 9781449316952

2. Healy, Kieran

Data Visualisation: A Practical Introduction

Princeton University Press. 2019.

ISBN: 9780691181622

3. Luciano Ramalho

Fluent Python: Clear, Concise, and Effective Programming

2nd ed. O’Reilly. 2022.

ISBN: 9781492056355

4. Marc Lutz

Learning Python: Powerful Object-Oriented Programming

6th ed. O’Reilly. 2025.

ISBN: 9781098171308

5. SAS Viya

Machine Learning Node Reference

SAS Institute Inc. 2023.

ISBN: 0000000000

6. Wickham, Hadley; Mine Çetinkaya-Rundel and Garrett Grolemund

R for Data Science (2nd ed.)

O’Reilly Media. 2023.

ISBN: 9781492097402

Objectives

The course ‘AI Techniques: Classification and Clustering’ aims to provide comprehensive training that combines sound theoretical knowledge with practical skills applicable in the field of Artificial Intelligence. Specific objectives include mastering the theoretical foundations of the main classification and clustering techniques, such as k-NN, SVM, Naive Bayes, K-means and DBSCAN, as well as understanding feature engineering as an essential process for optimising the performance of AI models. Furthermore, the course aims to familiarise students with the most widely used tools and libraries in the AI environment, such as Scikit-learn, Pandas and NumPy, so that they can apply this knowledge to practical problems.

In terms of skills, students will be able to evaluate and select the most appropriate algorithms for solving specific classification and clustering problems, design and carry out feature engineering processes to optimise datasets, implement practical solutions using industry-standard tools, and analyse the results obtained to fine-tune models effectively. Active participation in collaborative projects and discussions is also encouraged, fostering critical thinking and creative problem-solving.

Assessment for the module will combine the acquisition of theoretical knowledge with practical skills. Continuous assessment will account for 50 per cent of the final mark and will include active participation in forums and discussions, the completion and submission of individual or group practical assignments, and the completion of quizzes and self-assessment tasks. The remaining 50 per cent of the mark will be allocated to the final exam, which will assess both theoretical knowledge and the ability to apply it in a structured context. To pass the module, students must achieve a minimum mark of 5 out of 10 in both assessment components.

Course description

The course ‘AI Techniques: Classification and Clustering’ covers the theoretical and practical foundations required to apply advanced classification and clustering techniques in the field of Artificial Intelligence. It is structured into six core modules covering the following content:

Feature Engineering: An introduction to techniques for selecting and generating variables to optimise AI models. This module explores the fundamental principles for transforming and preparing data, thereby improving the effectiveness of algorithms.

Classification with SVMs (Support Vector Machines): Analysis of the theoretical foundations and practical applications of Support Vector Machines, a key statistical technique for classification.

Classification using k-Nearest Neighbours (k-NN): A study of the k-Nearest Neighbours algorithm, an essential tool for proximity-based classification, with an emphasis on its implementation and practical applications.

Classification with Naive Bayes: A review of the Naive Bayes probabilistic method, including its statistical foundations and its use in real-world classification scenarios.

Clustering with K-means: An introduction to this popular clustering method, focusing on its theoretical understanding, practical implementation and applications.

Clustering with DBSCAN (Density-Based Spatial Clustering of Applications with Noise): An introduction to the density-based clustering algorithm, ideal for working with complex and noisy datasets.

These modules have been designed to provide a thorough and practical understanding of the main classification and clustering techniques, encouraging active learning through activities and projects.

Assessment system and criteria

The assessment system for the module ‘AI Techniques: Classification and Clustering’ combines practical activities and knowledge tests, with the aim of measuring both the acquisition of theoretical skills and the ability to apply them in real-world contexts.

The final mark is divided into two main components:

Continuous assessment (50 per cent): This includes active participation in forums and discussions, the timely submission of Feedback exercises (1 and 2), and the completion of questionnaires or self-assessment tasks. These activities help to consolidate the concepts taught and assess the student’s progress throughout the course.

Final exam (50%): This consists of a test that assesses both the theoretical knowledge acquired and its application to practical problems. To pass the module, students must achieve a minimum mark of 5 out of 10 in both the continuous assessment and the final exam.

Continuous assessment aims to encourage active participation, collaborative work and practical learning, whilst the final exam ensures that essential knowledge has been understood and can be applied effectively.

Addendum

The course ‘AI Techniques: Classification and Clustering’ is delivered online, which allows for flexibility in learning and enables students to organise their time independently. The virtual sessions, both synchronous and asynchronous, are designed to encourage interaction between students and the lecturer, promoting active and collaborative learning.

Learning materials:

Students will have access to a variety of resources, including learning guides, theoretical content enriched with links and a bibliography, self-assessment exercises and practical activities. All material will be available in the virtual classroom for reference at any time.

Duration and course load:

The module carries 6 ECTS credits, comprising 22 hours of online classes and 18 hours of tutorials. These hours are supplemented by time spent on independent study and practical activities, ensuring a well-rounded education.

Teaching support:

The lecturer will be available to answer questions and provide support through weekly tutorials and the virtual classroom’s messaging system. Students are encouraged to use these channels to get the most out of the course.

Bibliography

Core:

1. Burger, Scott V.

Introduction to Machine Learning with R

O’Reilly. 2018.

ISBN: 9781491976449

2. Eric Matthes

Python Crash Course (3rd ed.)

No Starch Press. 2023.

ISBN: 9781593276034

3. Fernández-Avilés, Gema

Fundamentals of Data Science with R

McGraw Hill. 2024.

ISBN: 9788448636289

4. Lantz, Brett

Machine Learning with R (3rd ed.)

Packt Publishing Ltd. 2019.

ISBN: 9781788295864

Objectives

In terms of knowledge acquisition, the module aims to enable students to understand the key programming languages, libraries and frameworks used in artificial intelligence, as well as the statistical theories most commonly applied in this field. Students are also expected to be able to classify and compare different families of algorithms and to identify their practical applications.

With regard to the development of skills, students must develop the ability to evaluate AI-based solutions and determine their effectiveness. They must also be able to apply technologies and algorithms to solve specific problems and use deep learning techniques to tackle complex challenges.

As for assessment criteria, active participation in forums and discussions is required, as well as the completion and submission of individual and group assignments. The final exam accounts for 50 per cent of the final mark.

Course content

The course is organised into six modules. The Linear Regression module covers its theoretical foundations and practical applications for identifying linear relationships and making predictions. The Logistic Regression module addresses classification techniques based on probability assignment. The Random Forest Regression module introduces the use of decision trees to handle non-linear data and how to combine these to create more accurate and complex models. The Deep Learning and LSTM module explores neural networks and recurrent networks specialising in sequential data. The Convolutional Networks module focuses on three-dimensional feature extraction and its application in computer vision. Finally, the Reinforcement Learning module teaches reward-based learning techniques for sequential decision-making.

Assessment system and criteria

The assessment system includes continuous assessment, which accounts for 50 per cent of the final mark and takes into account participation in forums and the completion of individual and group practical activities. The remaining 50 per cent is based on the final exam. In the event of a fail, the resit session allows students to retake both the practical activities and the exam, provided they have followed the instructions and received the relevant feedback.

Addendum

Students are advised to keep a logbook to record their progress and to participate actively in the online classes, although these will also be available in recorded format for reference. Lecturers are available for tutorials, which can be requested via email or through the virtual campus messaging system. The suggested reading list includes texts on statistics, regression and deep learning, by authors recognised in both academic and professional circles.

Bibliography

Core:

1. David W. Hosmer, Jr., Stanley Lemeshow, Rodney X. Sturdivant

Applied Logistic Regression (3rd ed.)

Wiley. 2013.

ISBN: 9780470582473

2. Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining

Introduction to Linear Regression Analysis

Wiley. 2006.

ISBN: 9780471754954

3. Josh Patterson, Adam Gibson

Deep Learning: A Practitioner’s Approach

O’Reilly. 2017.

ISBN: 9781491914250

4. Joseph M. Hilbe

Logistic Regression Models

CRC Press. 2009.

ISBN: 9781420075755

5. Peter Bruce, Andrew Bruce and Peter Gedeck

Practical Statistics for Data Science

2nd ed. Marcombo. 2022.

ISBN: 9788426734433

6. Sheldon M. Ross

An Introduction to Statistics

Reverté. 2007.

ISBN: 9788429150391

Objectives

To understand the importance of explainability in AI models

To learn how to explain AI models using XAI with tools such as SHAP or alternatives.

To learn about and understand the key metrics associated with regression models

To recognise and understand the key metrics associated with classification models

Understand data imbalance and learn about balancing techniques

Course content

1. XAI: definition, concepts and properties

2. Stability

3. Introduction to AI model metrics

4. Performance metrics for regression models

5. Performance metrics for classification models

6. Balanced data

Assessment system and criteria

- Final exam for the module (70% of the mark).

- Practical assignment (30% of the mark).

Timetable

Click on this link to view the detailed timetable in Excel

Bibliography

Essential:

1. Kuhn, M. and Johnson, K.

Feature Engineering and Selection: A Practical Approach for Predictive Models

Chapman and Hall/CRC. 2019.

ISBN: 9781138079229

2. Provost, F., & Fawcett, T.

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

O’Reilly Media. 2013.

ISBN: 9781449374266

Supplementary:

3.- A. Barredo, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, R. Chatila, F. Herrera

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges towards responsible AI

Information Fusion (58) 82–115. 2020.

ISBN: 15662535

4. Iqbal H. Sarker

Machine Learning: Algorithms, Real-World Applications and Research Directions

SN Computer Science 2, 160. 2021.

ISBN: 2662995X