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.
Russ Salakhutdinov, Office: Pratt Building, Room 290F
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.
- 3 assigments: 40%
- 1-hour midterm 25%
- 2-hour final exam 35%
Midterm is on October 29, 2015:
Final is on TBD.
This course covers some of the theory and methodology
of statistical aspects of machine learning. The preliminary set of
topics to be covered
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
Christopher M. Bishop (2006)
Pattern Recognition and Machine Learning,
You can also use these books for additional reference:
Email: rsalakhu [at] cs [dot] toronto [dot] edu
Lecture Schedule |
CSC 411 (Fall 2015): Machine Learning