This is the homepage of the third year Computer Science course in Artificial Neural Networks at the Department of Technology, Örebro University.
Introduction
Course Contents
Teachers
Literature
Web Links
Labs
Examinations
Past Exam Papers
Matlab
Previous course (2004)
Artificial neural networks are parallel computing devices consisting of many interconnected simple processors. They share many characteristics of real biological neural networks such as the human brain. Knowledge is acquired by the network from its environment through a learning process, and this knowledge is stored in the connections strengths (weights) between processing units (neurons). In recent years, neural computing has emerged as a practical technology with applications in many fields. The majority of these applications are concerned with problems in pattern recognition, for example, in automatic quality control, optimization and feedback control. The course deals with classical pattern recognition, supervised and unsupervised learning using artificial neural networks, genetic algorithms, and applications of neural computing in artificial intelligence and robotics. The theoretical parts of the course will be tested by a number of computer based laboratory sessions ("laborations") using MATLAB.
Lectures:
Tom Duckett
E-mail: tom.duckett@tech.oru.se
Labs:
Malin Lindquist
E-mail: malin.lindquist@tech.oru.se
Henrik AndreassonThe lectures will be given in English, but you can ask questions in Swedish.
E-mail: henrik.andreasson@tech.oru.se
Dan W. Patterson, "Artificial Neural Networks: Theory and Applications", 1996, Prentice-Hall.
R. Beale and T. Jackson, "Neural Computing: An Introduction", 1990, Institute of Physics Publishing.
Christopher M. Bishop, "Neural Networks for Pattern Recognition", 1995, Oxford University Press.
Simon Haykin, "Neural Networks: A Comprehensive Foundation", 1998, Prentice-Hall.
M. Arbib, "The Handbook of Brain Theory and Neural Networks", 1995, MIT Press.
F. F. Soulié and P. Gallinari, "Industrial Applications of Neural Networks", 1998, World Scientific.
David E. Goldberg, "Genetic Algorithms in Search, Optimization & Machine Learning", 1989, Addisson-Wesley.
(Please mail me if you find any more useful links. :-)
The are six obligatory labs which all students must pass by presenting their work to the teachers. Each lab must be finished by the start of the next lab session. The penalty for missing more than one lab deadline will to complete one extra lab (at the discretion of the course leader).
Lab |
Name |
Instructions |
Data/M-files |
1 |
Introduction to pattern recognition |
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2 |
Minimum distance classifier and Bayes optimal classifier |
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3 |
Simple neuron models and training algorithms |
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4 |
Multi-layer feedforward networks and backpropagation |
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5 |
Self-organising feature maps and Hopfield networks |
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6 |
Sensory data analysis with an electronic nose |
data files |
All labs are carried out in Matlab. If you want to know more about Matlab there are some links below.
The labs should be done in teams of two people. There is one 4 hour lab session per week where the teachers will be present.
For the exact date, time and location see http://stormsvala.oru.se.
Week 12: ordinary examination.
May-June: first re-sit examination.
August: second re-sit examination.
Matlab should be installed on the department computers; to start it click on NTLinks - Matematik - Matlab 6.5.
There is a lot of on-line documentation in the form of pdf-files, an introduction to the Matlab environment, and all the toolboxes. Start a web browser, and then select Help - Help Desk (HTML) in Matlab.
Here's some links to Matlab related stuff: