Class Artificial Intelligence

This course presents the basic concepts and techniques of Artificial Intelligence (AI), with the subareas of AI, Machine Learning and DeepLearning. The work to be carried out by students using Machine Learning (ML) techniques and the development of supervised and unsupervised algorithms stands out.

Not applicable

1. Introduction to Artificial Intelligence: motivation, benefits and the type of problems it aims to solve. 2. Artificial Intelligence in Data Science. 3. Artificial Intelligence - Technologies that allow it to function. 4. Machine Learning - learning process. 5. Types of Machine Learning. 6. Machine Learning - Algorithm Typologies.     6.1. Supervised Algorithms     6.2. Unsupervised Algorithms 7. Development and implementation of machine learning algorithms.

The teaching methodology consists of the presentation and discussion of topics, and whenever possible present existing technologies, through the implementation of examples of applications that demonstrate the concepts involved. At the end of each topic, exercises are proposed to consolidate learning. Also, new teaching methodologies are explored with students getting involved in the exploration of new development and implementation techniques with support for machine learning. Assessment Method: Curriculum Assessment: 1.Assessment test to be carried out on 06/01/2025, with a weight of 60% in the final grade, and a minimum grade of 8 points. 2.Practical work with a weight of 30% in the final grade. 3.Attendance and participation in classes with an appreciation of 10%. Minimum of 70% attendance in classes. Final Assessment: Final exam to be held at a time of evaluation.

Oliveira, A. (2019). Inteligência Artificial. Ensaios da Fundação, Edição 2019, ISBN: 9789898943309, Fundação Francisco Manuel dos Santos. Costa, E., Simões, A. (2008). Inteligência Artificial ? Fundamentos e Aplicações, 2ª Ed. At. e Aum., Edição 2008, ISBN: 978-972-722-340-4, Editora: FCA. Russell R. & Norvig P. (2010) Artificial Intelligence: A Modern Approach. Third Edition, Prentice Hall. Nilsson, N. J. (2014). Principles of artificial intelligence. Morgan Kaufmann. Mitchell, M. (1998). An introduction to genetic algorithms. MIT press, 1998. Michalewicz, Z. (1996). Genetic Algorithms + data Structures = Evolution Programs , 3 rd edition, Springer Verlag, ISBN 3540606769, 1996.