Prerequisites: Background in linear algebra and statistics.

Description: This course introduces topics in machine learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods. Students are expected to implement several algorithms in Matlab and have some background in linear algebra and statistics.

Click on "Handouts" for more details.

Old Course: http://hunch.net/~coms-4771/

Bulletin Board: Class bulletin board (Click on Discussion)

Online Text Book: Introduction to Graphical Models Use your Columbia UNI as your login and then your last name as your password. For example "John Smith" would use the login "js2104" and password "Smith". Registered students only.