Intelligent Systems: Reasoning and Recognition - 4MMSIRR6

Goal(s)

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, artificial neural networks, unsupervised learning and support vector machines. 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.

Programming exercises are performed in Python and the NASA CLIPS symbolic reasoning programming environment.

All lectures are taught in English.

Content(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 notions of Symbolic Logic
Basic techniques from Probability and statistics
Linear Algebra
Object oriented programming.