CSC321 Spring 2012 - Tutorials

• January 12
  Demonstrations of some neural network learning algorithms
  The first two demonstrations are simply for your enjoyment. They will not be on any tests so do not read the papers unless you want to. The third demonstration will be explained at great length towards the end of the course. You can try reading about it now, but you may find it very hard to understand before you have the necessary background.
  Demo 1: Glovetalk: A net that converts hand-movements to speech. pdf
  Demo 2: NeuroAnimator: A net that learns to emulate a physical system. pdf
  Demo 3: A net that learns to recognize really bad handwritten characters flash demo
  

• January 19
  Introduction to Matlab for complete novices.
  Download the first matlab tutorial: first Matlab tutorial as .pdf.
  

• January 26
  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.
  Download the second Matlab tutorial: second Matlab tutorial as .pdf.
  Matlab code used in second tutorial:
  classdata1.m
  classdata2.m
  graddescent_simple.m
  graddescent_vectorized.m
  perceptron.m
  regressiondata.m
  showclassdecision.m
  

• Feb 2
  Tutorial on: Essential Concepts from Probability Theory.
  .pdf file for probability tutorial
  

• February 9
  Post mortem on assignment 1
  Some more demos of machine elanring successes.

• February 16
  Explanation of assignment 2
  Review of all material in the first 11 lectures.
  This is your last chance to ask questions before the midterm.

• March 1
  No Tutorial??

• March 8
  Post mortem on assignment 2
  Explanation of assignment 3

• March 15
  Extra lecture on Non-parameteric dimensionality reduction (not on exam)
  (notes as .ppt ) (notes as .pdf)
  

• March 22
  Lecture 19a: Learning Restricted Boltzmann Machines
  This lecture is required for the final exam.
  

• March 29
  Post mortem on assignment 3
  Extra lecture (not on exam)
  (notes as .ppt ) (notes as .pdf)
  

• April 5
  Review of all material in lectures 13-22.

CSC321 - Computation In Neural Networks: || www.cs.toronto.edu/~hinton/csc321/