STA 4273H (Fall 2011): Statistical Machine Learning

*** TUESDAY 9am - 12PM (Room UC 376) ***

Instructor: Ruslan Salakhutdinov; email rsalakhu at utstat dot toronto dot edu

Lecture Times: Tuesday 9am -- 12pm
Lecture Location: UC 376
First Lecture: Sep 13, 2011
Last Lecture: Dec 06, 2011
Office hours: Fridays 11-12 (TENTATIVE)

Prerequisite: Knowledge of statistical inference, probability theory, and linear algebra at the advanced undergraduate level, and some basic programming skills in R or Matlab. STA414/2104 is a plus, but is not required.

Marking Scheme
3 assignments worth 60%, one project worth 40%

Books :
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer.
Trevor Hastie, Robert Tibshirani, Jerome Friedman (2009) The Elements of Statistical Learning
David MacKay (2003) Information Theory, Inference, and Learning Algorithms

If you are not registered in the class, it is possible for you to audit it (sit in on the lectures), but only if you get the instructor's permission.

Course Description
This is an advanced graduate course, designed for Master's and Ph.D. level students, and will assume a reasonable degree of mathematical maturity. Specific topics to be covered include:

  • Linear methods for regression/classification
  • Model assessment and selection
  • Graphical models, Bayesian networks, Markov random fields, conditional random fields
  • Learning and inference in graphical models
  • Approximate variational inference, mean-field inference, loopy belief propagation
  • Basic sampling algorithms, Markov chain Monte Carlo, Gibbs sampling, and Metropolis-Hastings algorithm
  • Mixture models and generalized mixture models
  • Expectation-Maximization (EM) algorithm and variational EM
  • Unsupervised learning, probabilistic PCA, factor analysis, independent component analysis, and nonlinear dimensionality reduction.

We will also discuss recent advances in machine learning including
  • Deep learning
  • Deep Belief Networks and Deep Boltzmann Machines
  • Bayesian probabilistic matrix factorization and,
  • Hierarchical Bayesian models.

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STA 4273H (Fall 2011): Research Topics In Statistical Machine Learning ||