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CSC321 Spring 2016 -
Tutorials
The tutorials are Friday
1-2pm in CC 2130.
Tutorials will sometimes introduce new
material not covered in class.
Tutorial slides can be found here.
Tentative schedule:
- January 15:
Review of basic calculus and partial
derivatives.
Introduction to
Matlab for complete novices.
- January 22:
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.
- January 29:
Explanation of Assignment 1.
Three kinds of data and error: training,
validation, testing. [Needed for Assignment
1]
Review of back propagation (optional).
- February 5:
Review of probability theory.
.pdf
file for probability tutorial
- February 12:
Review of methods for speeding up learning
(Lecture 6). [Needed for Assignment 2]
- February
12:
Explanation of Assignment 2.
Post mortem on Assignment 1.
Last chance to ask questions before
midterm.
- February
19: No tutorial (Reading week)
- February 26:
Midterm test (starts at 1:10pm
sharp in tutorial)
- March
4:
Explanation of Assignment 3.
Review of clustering and the EM algorithm.
[Needed for Assignment 3]
Post mortem on Assignment
2.
- March 11:
Post mortem on the midterm.
Review of Boltzman machines and
simulated annealing (Lectures 11 and 12).
[Needed for Assignment 4]
- March 18:
Post mortem on Assignment 3.
- March 25: No
tutorial (Good Friday, university closed)
- April 1:
Review of stacked RBMs and deep networks.
Answer questions about Assignment 4.
Review for final exam.
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