Study of intelligent agents: problem solving, length and breadth search algorithms, game programming: minimax, exptimax, knowledge and reasoning, planning, learning, natural language processing, vision, robotics, inference mechanisms, Bayes networks, Markov processes, "Reinforcement learning" and their algorithms: TD and Q. Introduction to supervised and unsupervised machine learning: Decision trees, neural networks, support vector machines, K-NN and EM

Temps présentiel : 25 heures

Charge de travail étudiant : 100 heures

Méthode(s) d'évaluation : Examen écrit