
STA 414/2104 (Fall 2015): Statistical Methods for Machine Learning and Data Mining
Extra Office hours on Monday, Dec 14th, 12pm.
Instructor :

Russ Salakhutdinov, Office: Pratt Building, Room 290F
 Email:
rsalakhu [at] cs [dot] toronto [dot] edu
 Lectures: Mondays 25pm, MC 254
 Office hours: Mondays 11:00am  12:00pm in Pratt Building, Room 290F.
Marking Scheme:
 For undergraduate students
 3 assigments: 40%
 2hour midterm 20%
 3hour final exam 40%
 For graduate students
 3 assigments: 40%
 2hour midterm 20%
 3hour final exam 30%
 10% A 12minute individual presentation on a conference paper that you have read.
In class nidterm is on Oct 26th
You can use a nonprogrammable calculator and
an 8 by 11 inch Crib Sheet  Singlesided .
Final is on Thursday, Dec 17th, RW, 110, 117, 9  12am.
You can use a nonprogrammable calculator and
an 8 by 11 inch Crib Sheet  Doublesided .
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, Bayesian linear regression
 Linear models for classification
 Probabilistic Generative and
Discriminative models

Regularization methods

Model Comparison and BIC
 Neural Networks
 Radial basis function networks
 Kernel Methods, Gaussian processes, Support Vector Machines
 Mixture models and EM algorithm
 Graphical Models and Bayesian Networks
Prerequisite: Either STA302H or CSC411H
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
[
Home 
Assignments 
Lecture Schedule 
]
STA 414/2104 (Fall 2015): Statistical Methods for Machine Learning and Data Mining
 http://www.cs.toronto.edu/~rsalakhu/STA414_2015/
