Barnard College

COMS W 4771y Machine Learning

Topics from generative and discriminative machine learning including least squares methods, support vector machines, kernel methods, neural networks, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models and hidden Markov models. Algorithms implemented in Matlab. - T. Jebara
Prerequisites: Any introductory course in linear algebra and any introductory course in statistics are both required. Highly recommended: COMS W4701 or knowledge of Artificial Intelligence. General Education Requirement: Quantitative and Deductive Reasoning (QUA).
3 points Lect: 3.

Course
Number
Call Number/
Section
Days & Times/
Location
Instructor Enrollment
Spring 2014 :: COMS W4771
COMS
4771
76588
001
MW 1:10p - 2:25p
535 SEELEY W. MUDD BUILDING
I. Pe'er 74 / 100 [ More Info ]
Autumn 2014 :: COMS W4771
COMS
4771
24384
001
TuTh 1:10p - 2:25p
TBA
T. Jebara 32 / 150 [ More Info ]