Directory of Classes
NOTE: Course information changes frequently. Please re-visit these pages periodically for the most recent and up-to-date information.

Spring 2014 Computer Science W4771 section 001
MACHINE LEARNING

Call Number 76588
Day & Time
Location
MW 1:10pm-2:25pm
535 Seeley W. Mudd Building
Points 3
Approvals Required None
Instructor Itshack Pe'er
Type LECTURE
Course Description 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. 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.
Web Site CourseWorks
Department Computer Science
Enrollment 74 students (100 max) as of 6:57PM Saturday, April 19, 2014
Final Exam Day/Time May 12
M 1:10pm-4:00pm
Final Location 535 Seeley W. Mudd Building
Subject Computer Science
Number W4771
Section 001
Division Interfaculty
Open To Columbia College, Engineering and Applied Science: Undergraduate, General Studies, School of Continuing Education, Global Programs, Graduate School of Arts and Science, School of the Arts, International and Public Affairs, Barnard, Engineering and Applied Science: Graduate
Campus Morningside
Section key 20141COMS4771W001

Home      About This Directory      Online Bulletins      ColumbiaWeb
SIS update 04/19/14 18:57    web update 04/20/14 15:03