Machine Learning Course

Course Description

Course Description

This course provides an advanced introduction to machine learning.

Topics include:

Basic algorithm review (Data representation, Supervised learning, Unsupervised learning, Reinforcement learning, Semi-supervised learning)
Support vector machine;
Probabilistic topic model;
Conditional random fields;
Non-parametric learning;
Advanced topics on max-margin learning;
Sparse learning

Course Materials

There is no required text for this course. Notes will be posted periodically on the course web site.

The following books are recommended as optional reading:

Daphne Koller and Nir Friedman. Probabilistic Graphical Models. MIT Press, 2009.
Christopher M. Bishop. Pattern Recognition and Machine Learning, Springer, 2007.
John Hopcroft. Computer Science Theory for the Information Age. 2011.
Michael I. Jordan. An Introduction to Probabilistic Graphical Models. University of California, Berkeley. June 30, 2003.
Martin J. Wainwright and Michael I. Jordan. Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends in Machine Learning, V1 (1-2), 2008.
Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. Springer, 2003.
Yoshua Bengio. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, V2 (1), 2009.
David J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.

Prerequisites

Programming and other basic CS skills

Theories

Data representation (e.g., vector space model, language model)
Basic probability theory(e.g., likelihood, conditional probability, posterior probability, Bayes)
Basic linear algebra (e.g., linear transformations, eigenvalues, least-squares best fit)