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Fall 2014 Computer Science W4772 section 001
ADVANCED MACHINE LEARNING

Call Number 26927
Day & Time
Location
W 4:10pm-6:00pm
633 Seeley W. Mudd Building
Points 3
Approvals Required None
Instructor Daniel Hsu
Type LECTURE
Course Description Prerequisites: COMS W4771 or the instructor's permission; knowledge of linear algebra & introductory probability or statistics is required. An exploration of advanced machine learning tools for perception and behavior learning. How can machines perceive, learn from, and classify human activity computationally? Topics include Appearance-Based Models, Principal and Independent Components Analysis, Dimensionality Reduction, Kernel Methods, Manifold Learning, Latent Models, Regression, Classification, Bayesian Methods, Maximum Entropy Methods, Real-Time Tracking, Extended Kalman Filters, Time Series Prediction, Hidden Markov Models, Factorial HMMS, Input-Output HMMs, Markov Random Fields, Variational Methods, Dynamic Bayesian Networks, and Gaussian/Dirichlet Processes. Links to cognitive science.
Web Site CourseWorks
Department Computer Science
Enrollment 43 students (70 max) as of 7:29PM Sunday, October 19, 2014
Subject Computer Science
Number W4772
Section 001
Division Interfaculty
Open To School of the Arts, Barnard, Columbia College, Engineering and Applied Science: Undergraduate, Engineering and Applied Science: Graduate, Graduate School of Arts and Science, General Studies, School of Continuing Education, Global Programs, International and Public Affairs
Campus Morningside
Section key 20143COMS4772W001

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SIS update 10/19/14 19:29    web update 10/20/14 07:33