CSC 411 (Fall 2015): Introduction to Machine Learning

No office hours this Thursday, Nov 5.
PCA tutorial will start at 7pm on Thursday, Nov 5.

Instructor :
  • Russ Salakhutdinov, Office: Pratt Building, Room 290F
  • Email: rsalakhu [at] cs [dot] toronto [dot] edu

  • Lectures: Thursdays 6-8pm, BA1200
  • Tutorials: Thursdays 8-9pm
  • Office hours: Thursdays 1-2pm in Pratt Building, Room 290F.

Marking Scheme:

  • 3 assigments: 40%
  • 1-hour midterm 25%
  • 2-hour final exam 35%

Midterm is on October 29, 2015:

Final is on TBD.

Course Outline:

This course covers some of the theory and methodology of statistical aspects of machine learning. The preliminary set of topics to be covered include:

  • Linear methods for regression
  • Linear models for classification
  • Probabilistic Generative and Discriminative models
  • Regularization methods
  • Neural Networks
  • Support Vector Machines
  • Mixture models and EM algorithm
  • Reinforcement learning

Books :
Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer.

You can also use these books for additional reference:

Contact Information

Email: rsalakhu [at] cs [dot] toronto [dot] edu

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CSC 411 (Fall 2015): Machine Learning ||