Not previous knowledge is compulsory, but it is advisable to know programming in Python and to a lesser extent, Statistics.

General: 1. Analysis and synthesis skills 2. Planning and organization skills 3. Problem solving skills 4. Team work skills 5. Capacity to apply theoretical concepts into practical problems 6. Critical Reasoning skills Knowledge: 1. Knowledge about main Artificial Intelligence (AI) techniques and concepts. 2. Knowledge about the application of the different AI techniques in different areas, such as business, banking or finance. 3. Knowledge about the complexity in implementing intelligent solutions in real environments. Instrumental: 1. Designing intelligent systems to solve practical problems. 2. Critical analysis of real-life problems. 3. Using specific tools to develop intelligent systems. Attitude: 1. Creativity. 2. Quality aspects. 3. Motivation. 4. Seeking solutions to new problems.

1.- Introduction: definition of Big Data, Data Science and Artificial Intelligence. 2.- Descriptive Statistics: datasets and insights' 3.- Inferential Statistics: first algorithms of AI 4.- Classic AI: regression and decision trees. 5.- Deep learning: from perceptron to Neural Networks and Generative AI 6.- Large Language Models: from transformers to ChatGPT 7.- Applications and cases of use of Generative AI. 8.- Environmental impact of Generative AI 9.- Privacy: the ownership of data and the economics behind it. 10.- Business model of GAFAM (Google, Apple, Facebook, Amazon and Microsoft). 11.- Algorithms Audit: making AI more human and decreasing bias. 12.- Law ruling AI: risk management. 13.- AI Ethics: práctical application.

Learning activities: * Theoretical lectures: Mainly oriented to the acquisition of the theoretical knowledge of the subject' competences * Practical lectures: Mainly oriented to problem solving. Practical activities will include (preferably in Python) programming and the use of a public dataset. * (online or onsite) Personal Tutoring (asked by email in advance) Methodology: * Oral lectures in classroom * Problem solving