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.


Bulletin Board: Courseworks (Click on Discussion)

Online Text Book: Introduction to Graphical Models The book is available via courseworks. Please login using your CUNI email address (for example ab1234@columbia.edu) and your email password. Registered students only.

Academic Honesty Policy: Please read the policy here. By staying registered in the class you indicate your acceptance of all its terms. We do not accept late homework or absence without official reasons (medical, etc.) approved by a student dean. If you miss class, please coordinate with colleagues to find out what you missed (do not email the professor to help you catch up). If you find any of these terms unacceptable, please drop the class.

*To brush up on pre-requisites, we suggest the following books:
Strang, "Introduction to Linear Algebra," 4th edition
DeGroot and Schervish, "Probability and Statistics," 3rd edition
Feller, "Introduction to Probability," Volume 1