Course Objectives:

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning; unsupervised learning; learning theory; reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bio-informatics, speech recognition, and text and web data processing.

Course Requirements:

Students are expected to have the following background:

Ÿ Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program.

Ÿ Familiarity with the probability theory.

Course Contents:

Ÿ Introduction: basic concepts.

Ÿ Supervised learning: naive Bayes, support vector machines, feature selection, ensemble methods

Ÿ Learning theory: VC dimension, Bias

Ÿ Unsupervised learning: clustering, K-means, EM, PCA (Principal components analysis)

Reinforcement learning and control: MDPs, POMDPs, Bellman equations, value iteration and policy iteration, Q-learning, value function approximation.

Credits: 2