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
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