WCOMS4772-1: Advanced Machine Learning for Fall 2016

Days and Time

Wednesdays 4:10 AM-6:00 AM

Allowed For:

  • Masters
  • Professional
  • PhD

Prerequisites:

COMS W4771 or permission of instructor; knowledge of linear algebra & introductory probability or statistics is required

Notes:

None

Instructor:

Hsu, Daniel J

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, and Dynamic Bayesian Networks.