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Lectures, Readings and Due Dates

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CSC321 Spring 2014 - Tutorials

The tutorials are 11am-12pm on Wednesdays in IB220, and 10-11am 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 slidesMatlab 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 slidesMatlab 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.