

CSC321 Spring 2014 
Tutorials
The tutorials are 11am12pm
on Wednesdays in IB220, and 1011am on
Fridays in CC2150.
Tutorials will sometimes introduce new
material not covered in class.
Tutorial details are maintained by the
TA.
Tentative schedule:
 January 15 and 17:
Review of basic calculus.
Introduction
to Matlab for complete novices.
Tutorial
slides. Matlab
code.
 January 22 and 24:
Using
matlab for learning (very helpful
for assignment 1)
The tutorial will show how to use Matlab to
implement some of the simple learning
algorithms in the lectures. It is intended
mainly for Matlab novices, but it will also
help you understand the code in assignment
1.
Tutorial
slides. Matlab
code.
 January 29 and 31:
Explanation of Assignment 1.
Three kinds of data and error: training,
validation, testing.
Review of back propagation.
 February 5 and 7:
Tutorial on: Essential Concepts
from Probability Theory.
.pdf
file for probability tutorial
 February 12 and 14:
Post mortem on Assignment 1.
Discussion of Assignment 2.
 February
19 and 21: No tutorials (Reading week)
 February 26 and 28:
Review of combining models,
AdaBoost and/or recurrent neural networks.
 March 5 and 7:
Post mortem on Assignment 2.
Review for midterm.
Last chance to ask questions before midterm.
 March 12 and 14:
Explanation of Assignment 3.
Review of clustering and the EM algorithm.
 March 19 and 21:
Post mortem on the midterm.
Review of Boltzman machines and simulated
annealing.
 March 26 and 28:
Post mortem on Assignment 3.
Explanation of Assignment 4.
Review of Restricted Boltzman Machines and
multiple layers of features (stacked RBMs).
 April 2 and
4:
Review of stacked RBMs and deep networks.
Review of Assignment 4.
Review for final exam.


