S1. The History of Artificial Intelligence; Fundamentals of Statistics; Data Literacy;
S2. Fundamental notions of AI with emphasis on the search-based approach; Classical Artificial Intelligence (GOFAI);
S3. Machine Learning (ML); Types of Machine Learning; Genetic Algorithms;
S4. Neural Networks; Generative AI; Chatbots and Personal Agents; Transformer Architecture and GPT;
S5. Applications of Generative AI in different industries; Prompt Engineering; Data Analysis with Generative AI;
S6. Advances in Artificial Intelligence: Transformation in Public Administration and Innovations in the Private Sector;
S7. Introduction to User-Centered Design (UCD); Design of User-Centered Intelligent Services;
S8. Artificial Intelligence and Ethics.

The assessment of this curricular unit is conducted through a practical group project developed over the entire semester, which involves creating an AI-based system encompassing the various topics covered in the classes. The project accounts for 100% of the final grade and includes partial submissions of results at specific, pre-defined moments.
The partial submissions (E1, E2, and E3) correspond to stages of the project and have the following descriptions and respective weightings towards the final grade:

- E1: Initial project planning and analysis (30%)
- E2: Design and partial conception of the AI system (30%)
- E3: Complete final project and presentation (40%)

Individual assessment of each student will be determined through participation in presentations, group discussions, and effective contribution at each stage of the project. Individual oral discussions may be held to ensure the contribution and understanding of each group member.
Attendance in classes is not mandatory but is strongly recommended for proper project guidance and clarification of any doubts.

Mandatory

Machine Learning For Dummies de Luca Massaron e John Paul Mueller

Optional