Transcript Artificial Intelligence

AI – CS289
Machine Learning - Labs
Machine Learning – Lab 4
02nd November 2006
Dr Bogdan L. Vrusias
[email protected]
AI – CS289
Machine Learning - Labs
Instuctions
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The following slides demonstrate the capabilities of Machine Learning.
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The examples are taken from:
– Negnevitsky, M., "Artificial Intelligence: A Guide to Intelligent Systems", 2nd edn.
Addison Wesley, Harlow, England, 2005.
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Download and unzip the following file, that contains all examples:
http://www.booksites.net/download/negnevitsky2/student_files/matlab/0321204662_matlab.zip
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or alternately:
http://www.cs.surrey.ac.uk/teaching/cs289/lecturenotes/0321204662_matlab.zip
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To run the examples, use Matlab (left hand side window called current
directory) to navigate to the directory where you have downloaded and
unzipped the files, and then simply type (case sensitive) the name of the file
without the “.m” extention.
– E.g. to run the Perceptron_AND.m you type Perceptron_AND on Matlab’s
command window.
02nd November 2006
Bogdan L. Vrusias © 2006
2
AI – CS289
Machine Learning - Labs
The perceptron: learning linearly separable
functions
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Filename: Perceptron_AND.m
Matlab command: Perceptron_AND
Problem: Two-input perceptron is required to perform logical operation AND
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Run the file, follow the instructions, READ THE COMMENTS on each step,
and observe the following:
– The graph should show you how well it separates
the two categories (category 0 and category 1)
– The input pairs ([0,0], [0,1], [1,0], [1,1]) are
classified correctly after the training.
02nd November 2006
Bogdan L. Vrusias © 2006
3
AI – CS289
Machine Learning - Labs
The perceptron: learning linearly separable
functions
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Filename: Perceptron_OR.m
Matlab command: Perceptron_OR
Problem: Two-input perceptron is required to perform logical operation OR
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Run the file, follow the instructions, READ THE COMMENTS on each step,
and observe the following:
– The graph should show you how well it separates
the two categories (category 0 and category 1)
– Observe how the categorisation line changes
for every step of the training.
– The input pairs ([0,0], [0,1], [1,0], [1,1]) are
classified correctly after the training.
02nd November 2006
Bogdan L. Vrusias © 2006
4
AI – CS289
Machine Learning - Labs
The perceptron: an attempt to learn linearly nonseparable functions
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Filename: Perceptron_XOR.m
Matlab command: Perceptron_XOR
Problem: Two-input perceptron is required to perform logical operation XOR
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Run the file, follow the instructions, READ THE COMMENTS on each step,
and observe the following:
– The graph should show you how well it separates
the two categories (category 0 and category 1)
– Observe how the categorisation line changes
for every step of the training. Does the system learn?
– The input pairs ([0,0], [0,1], [1,0], [1,1]) are
NOT classified correctly after the training.
– Can you tell why?
02nd November 2006
Bogdan L. Vrusias © 2006
5
AI – CS289
Machine Learning - Labs
Back-propagation algorithm
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Filename: XOR_bp.m
Matlab command: XOR_bp
Problem: The three-layer back-propagation network is required to perform
logical operation Exclusive-OR.
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Run the file, follow the instructions, READ THE
COMMENTS on each step, and observe the following:
– The graph should show you how the training
is performed. The blue line represents the error
that drops through time (iterations).
– Observe how there are now two line to separate the
categories (from the two hidden neurons)
– The input pairs ([0,0], [0,1], [1,0], [1,1]) are
classified correctly after the training.
02nd November 2006
Bogdan L. Vrusias © 2006
6
AI – CS289
Machine Learning - Labs
Competitive learning
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Filename: Competitive.m
Matlab command: Competitive
Problem: A single-layer competitive network is required to classify a set of
two-element input vectors into four natural classes.
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Run the file, follow the instructions, READ THE
COMMENTS on each step, and observe the following:
– The graph should show you the coordinates of the
input data vectors (red) together with the weight
vectors (blue)
– Observe how there are labels given to each cluster.
– The test vectors (green) are then labelled based on how
close they fall to a formed labelled cluster.
02nd November 2006
Bogdan L. Vrusias © 2006
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