Master in Artificial Intelligence Online

The module focuses on core aspects of artificial intelligence (AI), specifically problem-solving, automatic reasoning and planning. It establishes the general framework of AI, core concepts, definitions and the scope of AI, and covers historical aspects and the evolution of artificial intelligence, highlighting its impact on society and practical applications in various sectors. It also explores problem-solving as a core competence in artificial intelligence, introduces automatic reasoning and planning, explores structural representations of knowledge, and introduces models and techniques for automatic reasoning and paradigms for imprecise and uncertain reasoning.

The course focuses on the use of Python and cloud computing platforms to develop data science skills. Students will learn advanced data science-oriented programming in Python, explore key libraries such as NumPy and Pandas, and use cloud environments (AWS) to manage large data sets.

This module covers the core concepts of data mining, including cleaning and preparation techniques for intelligent analysis. It focuses on efficient indexing techniques and advanced structures for managing large volumes of data. It also covers data preparation for low-latency visualisation and the creation of dashboards integrating multiple sources of information.

The module covers three main paradigms:

• Supervised: techniques and mechanisms for training models with labelled data will be reviewed, enabling predictions or classifications to be made.
• Unsupervised: techniques will be used to process unlabelled data in order to discover patterns and structures inherently present in the data sets.
• Deep Learning: using deep neural networks, techniques will be used to learn complex representations and perform advanced AI tasks such as image recognition or natural language processing.

Advanced learning mechanisms will also be reviewed, such as reinforcement learning, transfer learning, and generative learning.

The module will review various techniques for analysing and understanding human language. Techniques will be reviewed that delve into:

• Tokenisation and Segmentation: the stage of dividing text into smaller units to facilitate analysis.
• Morphological and Syntactic Analysis: techniques for analysing the grammatical structure of words and how they combine to form sentences will be reviewed, enabling an understanding of the syntax and morphology of language.
• Feature extraction: techniques for identifying important features in the text, such as named entities, relationships between words and key concepts, will be reviewed in order to represent information in a meaningful way.
• Semantic Disambiguation: Techniques for resolving ambiguities in the meaning of words or phrases will be applied, improving accurate understanding of context and semantic interpretation.
• Language Generation: The main and most innovative technologies currently being used to autonomously generate coherent and relevant natural language will be reviewed.

This module focuses on training machines to interpret and understand visual information. Techniques will be reviewed that delve into:

• Object Recognition: techniques for identifying and classifying objects in images or videos will be reviewed, which is essential for applications such as facial recognition, object detection, and scene analysis.
• Image Segmentation: techniques for dividing images into regions or segments for more detailed analysis are reviewed. Segmentation is key to tasks such as contour identification, separation of foreground and background objects, and delimitation of fields of interest.
• Motion Detection and Tracking: techniques for tracking the movement of objects over time are reviewed.
• Pattern and Feature Recognition: Techniques for recognising complex visual patterns and extracting distinctive features to understand visual information are reviewed.
• Generative image models. Self-supervised learning models and autoencoders in computer vision.

• Decision-making.
• Risk analysis.
• Big Data and IoT.
• RPA.
• Chatbots.
• Recommendation systems.

• Health and Medicine: Medical diagnosis, disease prediction, development of personalised treatments, efficient management of patient records.
• Finance and Banking: Risk analysis, fraud detection, automated financial advice, portfolio management, automatic transaction processing.
• Commerce and marketing: Personalised recommendations, user behaviour analysis, inventory management, price optimisation, automated customer service.
• Education: Personalised learning, automated assessment, student performance analysis, online assistants for distance learning.
• Manufacturing and Supply Chain: automated quality control, predictive machinery maintenance, supply chain optimisation, inventory management.
• Human resources: Recruitment, CV analysis, talent management, recruitment process automation, performance assessment.
• Transportation and Logistics: Route optimisation, fleet management, asset monitoring, logistics planning, autonomous transport systems.
• Cybersecurity and Surveillance: threat detection, facial recognition, real-time behaviour analysis, perimeter surveillance, automatic response to security events.

This module focuses on understanding and communicating AI model decisions, exploring techniques to improve transparency. It examines ethical issues in the design and application of algorithms, considering bias and Social responsibility. It also addresses the regulatory framework that guides the development of artificial intelligence, highlighting emerging regulations and ethical standards. Students will analyse case studies, discuss ethical dilemmas and explore strategies for balancing technological innovation with ethical considerations and regulatory compliance.

This master's degree is 100% online. You can take the exams and complete the Master's Thesis online.

Programme Director

• Dr Laura García Cuenca
  Senior Lecturer at Universidad Europea de Madrid, attached to the School of Architecture, Engineering and Design. A certified doctor in Control Engineering and Intelligent Systems, her research line focuses on the development of intelligent systems applied to autonomous driving, data mining and machine learning.
  With a solid academic background, she combines teaching with the academic direction of the Master's degree in Continuing Education in Artificial Intelligence. Her research activity includes numerous scientific publications at international conferences (IEEE, Electronics, Sensors) and collaborations with national and international institutions. ​​
  In addition, she has held academic management positions, leading processes of digitisation, educational quality, and Student Affairs at Universidad Europea.​​
  She is the author of several educational books on artificial intelligence applied to business and marketing, as well as on systems administration and technical teaching.

Faculty

• Dr Eva Maria Andres
  PhD in Quantum Computing and AI from the University of Granada and MBA from Camilo José Cela University. She has extensive experience as a senior manager in various fields of IT, leading global teams in infrastructure, development and innovation. ​​
  She has worked on projects involving deep learning, reinforcement learning (RL), evolutionary algorithms and quantum AI. She collaborates with the University of Granada, research centres, journals and start-ups on the research and application of quantum technology in industry.
• Dr David Díaz Vico
  PhD in Computer and Telecommunications Engineering, Master's degree in Mathematics and Applications. Master's degree in Computer and Telecommunications Engineering. Bachelor in Mathematics and Computer Engineering from the Autonomous University of Madrid.
  Data Scientist at the Institute of Knowledge Engineering for 8 years, R&D Engineer at Telefónica I+D for 2 years and analyst at Accenture Analytics for 2 years. Professor at various universities and Business Schools for more than 10 years.​​
  Specialised in Machine Learning and particularly in Neural Networks, Deep Learning, Natural Language Processing and Computer Vision, fields to which most of his scientific publications and patents belong.
• Dr. Andrés Soto Villaverde
  PhD in Computer Science from the University of Castilla La Mancha. Research areas: Artificial Intelligence, Multi-agent Systems, Natural Language Processing, Machine Learning, Knowledge Representation.​​
  Professor of Computer Science at universities in Spain, the United States, Germany, Cuba, Venezuela and Mexico. Experience as a data scientist in commercial companies in Spain, Belgium and Germany, among others.
• Dr Jorge Moratalla Collado
  Jorge holds a PhD in Computer Science from Rey Juan Carlos University and a degree in Computer Engineering from the Polytechnic University of Madrid. He has completed several master's degrees related to Artificial Intelligence and Big Data, as well as an MBA specialising in ICT project management.​
  He combines his work as Head of the Artificial Intelligence and Automation Field at the public business entity Red.es with teaching at various Spanish universities.
• Dr Álvaro Manuel Rodríguez
  Senior Industrial Engineer (University of Oviedo). MBA (IUDE). Master's degree in Operations (University of Barcelona), Bachelor's Degree in Social and Cultural Anthropology (UCAM). Master's degree in Logistics (ESADE). Doctorate from the University of Valencia and doctoral candidate at the University of Oviedo. ​
  Researcher and lecturer in various Bachelor's Degrees in Cybersecurity, Logistics, Engineering and in various Master's degrees in Artificial Intelligence, Big Data and new technologies. His research and publications focus on the application of AI and Machine Learning tools in the field of health.
• Dr Álvaro Calle Cordón
  Extensive academic training in quantitative subjects and extensive experience in R&D projects in academia and industry. He completed a Master's degree in Physics and Mathematics and a PhD in Physics from the University of Granada. He has been a research scientist and lecturer at prestigious research centres in the US and European universities. ​
  He is the author of more than 20 scientific publications in peer-reviewed journals and a speaker at numerous international conferences. His focus is on the implementation of advanced analytical solutions in different sectors of banking and industry, for which he uses artificial intelligence and big data techniques.
• Dr Wolfram Rozas
  Dr. Rozas is a quantitative economist with a PhD in industrial technologies. He is an expert in the use of all exponential technologies, Big Data and Business Analytics, Artificial Intelligence, Cloud, Internet of Things, Blockchain and Quantum Computing to achieve strategic business objectives. ​
  He has more than 25 years of experience in companies such as PWC and IBM managing Business Intelligence, Business Analytics, Machine Learning, Deep Learning projects and implementing Cognitive Systems in the industrial sectors of Finance, Telecommunications, Distribution, Tourism and Transport, Mass Consumption, Energy and Public Services, Chemicals and Petroleum. ​
  He has collaborated as an expert in Business Intelligence, Big Data & Business Analytics and Artificial Intelligence at Business Schools and universities​ .
• Francisco Javier Diez
  As an electronic engineer, I have worked at IBM in consulting positions for the financial and insurance sectors.​
  Projects in Artificial Intelligence for Natural Language Interpretation and Insurance Claims Document Extraction, Virtual Assistants and Automated Generation of Emails to Customers.
• Jorge Erice
  Extensive experience in the design, implementation and support of data networks, extensive experience in the deployment and implementation of new functionalities and services, as well as associated infrastructures. All this is the result of many years of leadership in highly complex multinational projects, managing teams with members from different countries for the deployment of data networks throughout Europe, the Middle East and Africa.​
  Extensive experience in teaching years on data network technology and architecture, Agile methodology, Quantum Computing, etc., to IBM customers and employees.​
  Interested in continuing in the field of networking, new technology networks and high-speed solutions, as well as training in these and other areas.
• Dr Ramón Rizo Gómez
  Doctor of Psychology. Master's degree in Law and Master's degree in Data Protection, Transparency and Access to Information. Accredited as a Data Protection Officer (DPO) and Compliance Advanced. He is a professor at the School of Architecture, Engineering and Design at Universidad Europea de Madrid, teaching courses related to Behavioural Sciences and Legal Sciences.​
  He is interested in research on the protection of fundamental rights and ethics in relation to personal data, developments in information and communication technologies, and artificial intelligence.
• Dr Álvaro Brandon Hernández​
  Doctor in Artificial Intelligence with experience in data science and the technology sector. He works as a Senior Data Scientist at Lingokids, where he leads the development of machine learning models and data analysis to improve the educational experience of children.​
  He is a university lecturer in advanced AI and computational optimisation. He has published relevant research and participated in international conferences in the field of big data and machine learning.​

It is recommended. If you have no programming experience, Universidad Europea offers a year in Python so that you can learn the necessary skills before starting the master's degree.

Yes, the online Master's degree in Artificial Intelligence includes practical projects with real datasets and collaboration with companies in the technology sector, allowing you to gain applied experience and improve your employability from day one.

You do not need a computer with special requirements to take the online Master's degree in Artificial Intelligence. It is only important to have an up-to-date computer and a stable Internet connection to access the Virtual Campus and follow the classes without interruptions. The practical resources and tools are provided by the university itself, which guarantees that you will be able to work normally throughout the programme.

This Master's degree is designed to go far beyond technology. It aims to help you understand how to apply AI strategically in real contexts, so that you can differentiate yourself professionally in a world that is constantly changing, especially in certain sectors.

In healthcare: you will be able to work on the development of AI-assisted diagnostic systems, medical image analysis, personalised medicine, or disease prediction using predictive models. These are roles in demand by hospitals, medical insurers, and medical technology start-ups.

In banking: you will be able to design real-time fraud detection models, credit scoring systems with machine learning, and tools for compliance automation. Banks and fintechs are looking for profiles with these Competencies to gain agility and security.

In digital marketing: AI allows for more precise audience segmentation, automated advertising campaigns, analysis of consumer behaviour patterns, and optimisation of return on investment (ROI). Marketing agencies and departments require AI experts who are proficient in predictive analytics and automation platforms.

And beyond: you can also apply your knowledge in sectors such as logistics (route optimisation, predictive maintenance), energy (smart grid management), or even in product development with generative AI.

During the master's degree, you will learn to design, train and evaluate machine learning and deep learning models applied to real-world cases such as computer vision, natural language processing and predictive analytics, competencies that are in high demand by companies in all sectors.

Yes, the online master's degree in artificial intelligence has the same official validity and professional recognition as an on-campus degree, with the advantage of flexibility to study from anywhere without sacrificing academic quality or job opportunities.