STA 4273H (Fall 2011): Statistical Machine Learning

Lectures: Tuesdays 9:10am to 12:00pm in UC 376

Instructor :
  • Ruslan (Russ) Salakhutdinov, Office: SS6002,
  • Email: rsalakhu [at] utstat [dot] toronto [dot] edu

  • Lectures: Tuesdays 9:10am to 12:00pm in UC 376
  • First Lecture: : Sep 13, 2011.
  • Last Lecture: : Dec 06, 2011.
  • Office hours: Fridays, 11-12.

Course Outline:

Statistical machine learning is a very dynamic field that lies at the intersection of Statistics and computational sciences. The goal of statistical machine learning is to develop algorithms that can "learn" from data using statistical and computational methods. Over the last decade, numerous research fields, such as computational biology, neuroscience, artificial intelligence, data mining, signal processing, finance have been strongly influenced by advances in machine learning.

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.

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.

Books :

Contact Information

Email: rsalakhu [at] utstat [dot] toronto [dot] edu
Office: Sidney Smith Hall, Room 6002

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