Class Artificial Intelligence

This course introduces the basic concepts and techniques of Artificial Intelligence (AI) related to optimization.

Not applicable

  Introduction to Artificial Intelligence: motivation, benefits, and the types of problems it aims to solve. Artificial Intelligence in Data Science. Artificial Intelligence - Metaheuristics and Optimization. Local Search Metaheuristics. Global Search Metaheuristics: Genetic Algorithms. Global Search Metaheuristics: Particle Swarm Optimization. Development and Implementation of Optimization 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.

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.