CS2411 Subsymbolic Artificial Intelligence and Neural Networks
2004-2005
FAQ|
Announcements|
Course
Timetable|
Handouts|
Lab Information|
Example
Class Sheets|
Reading Lists|
Links
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-
Ask a question
or search the FAQ. Click the "email" button on the FAQ page to
submit a question to the FAQ. Or browse the questions already asked (there
is not much on it as yet.)
Announcements
- 11/1/05 - A sheet giving some exam
advice is available.
- 30/9/04 - Monday lab and examples class sessions will not
run, (unless class size increases greatly).
- 30/09/04 - Welcome
- Course starts today.
Lab and Example Class Timetable
Course Handouts
-
First day handout:
-
General information
about the course
- Neural Networks I:
General aspects of neural networks
and simple perceptrons, as
-
gzipped
Postscript or as
PDF.
- Generalization and Testing :
Elements of machine
learning, and how to test a learned system. As
-
gzipped
Postscript or as
PDF.
- Neural Networks II:
Multi-layer perceptrons and gradient descent learning.
As
-
gzipped
Postscript or as
PDF.
- Slides from a lecture on applications of MLPs
-
as PDF .
- Neural Networks III:
Improving performance of neural networks.
As
-
gzipped
Postscript or as
PDF.
- Probability I:
A review of probability and Bayes Rule.
-
as gzipped Postscript or as PDF
.
- Bayesian Classification:
Bayesian Classification and
Probability Estimation.
-
as Postscript or as PDF
.
- Document Classification:
Bayesian classification as
applied to document classification. Also includes a section on on-line
learning of probability estimates.
-
as Postscript or as PDF
.
- Decision Theory:
Given the probabilities of the
classes, what is the best classification to make. Includes discussion
of risk, and ROC curves.
-
as Postscript or as PDF
.
- Search I
- The general problem of search, greedy search and hill-climbing
as postscript
or .
as PDF .
- Search II
- Genetic Algorithms
as postscript or .
as PDF .
Lab Information
-
Matlab Tutorial:
-
Matlab
Tutorial
in PDF or
Matlab Tutorial
in Postscript.
This tells gives an introduction to Matlab. Work through this before
the first lab. There are problems in the tutorial, but they are not a
part of the lab; they are just to help you learn Matlab. They are
optional.
-
Lab 1: Introduction to Matlab and Classification
-
Description of
Lab 1
-
Lab 2: Classification using Multilayer Perceptrons
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Instructions for
Lab 2
-
Lab 3: Bayesian Spam Filter
-
Commands for Lab 3 in PDF
Note: Lab 3 is a two session lab.
Hints for Lab 3
-
Lab 4: Genetic Algorithms
-
Commands for Lab 4 in PDF
Examples Classes
Questions
-
First Examples Class questions This is a sheet of the questions
only.
- Second
Examples Class questions
- Third
Examples Class questions
- Fourth
Examples Class questions
- Fifth
Examples Class questions
Answers
- Answers to the first Examples Class questions as PDF
or as postscript
.
- Answers
to second Examples Class questions
- Answers
to third Examples Class questions
- Answers
to fourth Examples Class questions
- Answers
to fifth Examples Class questions
Reading Lists by Topic
A reading list by topic is here.
Links
-
A page on Multi-layer Perceptrons
- from Jim
Marshall at Pomona College. Includes backpropagation and applications.
- An
Introduction to Neural Networks. An on-line book by Krose and
van der Smagt.
- Look at section 3.1 and 3.2 on perceptrons, and chapter 4 on
backpropagation (but we need not worry about the mathematical details
of back propagation. Focus on the performance details.)
-
Ant Colony Optimization
Applet
-
I mentioned ant colony optimization for the TSP in the first lecture as
an example of the subsymbolic approach to AI. Here is a link to a Applet
created by Mark C. Sinclair showing this running on a very simple
problem.
-
Elastic Net Applet
-
The elastic net was mentioned in the first lecture as
an example of the subsymbolic approach to AI. Here is a link to a Applet
created by Alexander Budnik and Tanya Filipova showing this running
on a very simple problem.