Systèmes intelligents : reconnaissance et raisonnement (en anglais) - 4MMSIRR6

Objectif(s)

This course is taught in English.

This course provides an introduction to techniques for constructing systems that exhibit human level intelligence. The first half of the class covers techniques for machine learning and pattern recognition, including Bayesian learning, clustering, support vector machines, artificial neural networks, and deep learning architectures. The second half of the class covers techniques for symbolic reasoning systems including planning and problem solving, diagnostic reasoning, rule based programming, reasoning with temporal and spatial relations, and causal reasoning with Bayesian networks.

The course includes a Neural Network programming exercise performed with Keras and PyTorch in Python.

Contenu(s)

1. Introduction to intelligent systems.
2. Performance Evaluations
3. Bayesian Machine learning
4. Non supervised learning and clustering with K-means and EM
5. Artificial Neural Networks and deep learning.
6. Decision Trees and random forests.
7. Planning and problem solving.
8. Rule based systems
9. Reasoning with spatial, temporal and other forms of relations.
10. Diagnostic and Causal reasoning with Bayesian networks and probabilistic graph models.

Basic Probability and Statistics
Linear Algebra
Experience with Python is a plus.