Prerequisites: Background in calculus, linear algebra, and statistics.
Programming ability in some (any) language.

Description: The course introduces various topics in machine learning. Material will include:
Baeysian inference & decision theory, Gaussian and exponential family distributions,
maximum likelihood, least squares, linear regression, linear classification,
neural networks, statistical learning theory, support vector machines, kernel methods,
mixture models, the EM algorithm, graphical models, and hidden Markov models.
Students are expected to implement several algorithms in Matlab, and have some
background in calculus, 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).
Once a particular grade is posted for you on Courseworks for any homework or midterm,
you have two weeks to contest them. Afterwards, these grades cannot be changed (do not wait
until the end of the semester to contest any grading issues that are more than two weeks old).
This course assumes you have the ability to upload your work via courseworks and can
figure out how to attach files. If you are incapable of using courseworks, unable to program,
or unable to follow mathematical notation, please drop the class. If you find any of these
terms unacceptable, please drop the class.