CSC321 Spring 2008 - Tutorials
The tutorials are 12.00-1.00 on thursdays.
RW 117 (same place as the lectures)
The schedule below may be changed.
- January 10
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 17
Introduction to Matlab for complete novices.
Download the first matlab tutorial:
first Matlab tutorial as .pdf.
- January 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 for Matlab novices.
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
- Jan 31
Tutorial on: Essential Concepts from Probability Theory.
.pdf file for this years probability tutorial
.ps file for last years probability tutorial
- February 7
Post mortem on assignment 1
Explanation of assignment 2
- February 14
Review of all material in the first 11 lectures.
This is your last chance to ask questions before the midterm.
- February 28
Tutorial Lecture 1 on Support Vector Machines
The material in this lecture will not be in the exam or assignments
(notes as .ppt )
(notes for all browsers))
(notes as .ps, 4 per page))
- March 6
Explanation of assignment 3
Post mortem on assignment 2
- March 13
Post mortem on assignment 3
Explanation of assignment 4
Some worked examples of the computations used in Hidden Markov Models
- March 20
Tutorial Lecture 2 on Support Vector Machines
The material in this lecture will not be in the exam or assignments
(notes as .ppt )
(notes for all browsers))
(notes as .ps, 4 per page))
- March 27
Extra lecture on ISOMAP & Stochastic Neighbor Embedding (not
on exam)
(notes as .ppt )
(notes for all browsers))
(notes as .ps, 4 per page))
- April 3
Post mortem on assignment 4
Explanation of assignment 5
- April 10
Review of all material in lectures 13-25.
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