WCOMS4771-2: Machine Learning for Spring 2018

Days and Time

Mondays and Wednesdays 2:40 PM-3:55 PM

Location

RTBA BTBA

Allowed For:

  • Undergraduate
  • Masters
  • Professional
  • PhD

Prerequisites:

Any introductory course in linear algebra and any introductory course in statistics are both required. Knowledge of W4701 Artificial Intelligence is highly recommended.

Notes:

None

Instructor:

Verma, Nakul

Description

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.