COMS W4772x - Advanced Machine Learning

Institution:

Barnard College

Subject:

Description:

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. - T. Jebara Prerequisites: COMS W4771 or permission of instructor; knowledge of linear algebra & introductory probability or statistics is required. General Education Requirement: Quantitative and Deductive Reasoning (QUA). 3 points

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.barnard.edu

Phone Number:

(212) 854-5262

Regional Accreditation:

Middle States Association of Colleges and Schools

Calendar System:

Semester

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