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Fall 2014 Computer Science W4772 section 001

Call Number 26927
Day & Time
W 4:10pm-6:00pm
To be announced
Points 3
Approvals Required None
Instructor Daniel Hsu
Course Description Prerequisites: COMS W4771 or permission of instructor; 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 57 students (60 max) as of 11:30PM Wednesday, July 23, 2014
Subject Computer Science
Number W4772
Section 001
Division Interfaculty
Open To Columbia College, Engineering and Applied Science: Undergraduate, General Studies, Graduate School of Arts and Science, Engineering and Applied Science: Graduate, Barnard
Campus Morningside
Section key 20143COMS4772W001

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