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Rzeszów University of Technology
Electrical and Computer Engineering
The Faculty of:
Field of study:
Computer Science
Speciality:
BSc
Study degree (BSc, MSc):
COURSE UNIT DESCRIPTION
Artificial Intelligence
Course title:
Lecturer responsible for course: Jacek Kluska, PhD, Assoc. Prof.
Contacts: phone: +48 17 8651247
e-mail: [email protected]
Department : Computer Science and Automatic Control
Type of classes
Semester
6
Weekly load
L
Lectures
20
C
Theoretical
Classes
10
Lb
Laboratory
10
P
Project
Number of
ECTS
credits
5
Course description
Lecture:
Soft computing. Fuziness vs. probability. Generalized operators in multivalued logics. Knowledge-base
representation. Inference methods. Methodology of generalized expert system design. Fuzzy expert systems.
Artificial neural networks. Classification. Linear separability. Multilayer networks. Examples of learning.
Backpropagation neural networks. Adaptive linear neuron. Wiener-Hoff equation. Newton-Raphson algorithm.
Ideal gradient descent method. Widrow-Hoff delta rule. Recursive least squares algorithm. Selforganizing
networks. CP-networks. Hopfield networks. Applications of recurrent networks. Support Vector Machines.
Classification and regression. Optimal separating hyperplane. Kernel functions. Applications of SVM in medical
diagnosis.
Classes:
Fuzzy expert systems as real-time controllers (navigation system for mobile robots). Medical diagnostic systems.
Learning algorithms for OCR (perceptrons, Hopfield networks). Adaptive linear networks. Efficiency of learning
methods. Speech recognition. Cancer diagnosis using SVM.
Laboratory:
Fuzzy expert systems as real-time controllers (navigation system for mobile robots). Medical diagnostic systems.
Learning algorithms for OCR (perceptrons, Hopfield networks). Adaptive linear networks. Efficiency of learning
methods. Speech recognition. Cancer diagnosis using SVM.
Project:
Objectives of the course
The goal of the course is to learn about computer systems that exhibit intelligent behavior. Topics include fuzzy
expert systems, neural networks, optimal classification and regression methods. Most of practical problems
concern with robotic systems and medical applications.
Examination method
Written solution of design problems and oral discussion.
Bibliography
1. S. Gunn, Support Vector Machines for Classification and Regression, University of Southampton, 1998
2. Matlab User’s Guide, The MathWorks Inc., 1999.
3. S. Haykin, Neural Networks – a Comprehensive Foundation, Macmillan College Publishing Company, New
York, 1994.
4. Kosko B., Neural Networks and Fuzzy Systems. A Dynamical Systems Approach to Machine Intelligence.
Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1992.
5. Peterson L. E. and Coleman M. A., "Machine learning-based receiver operating characteristic (ROC) curves
for crisp and fuzzy classification of DNA microarrays in cancer research", Int. J. Approximate Reasoning, Vol.
47, pp. 17-36, 2008.
Lecturer signature
Head of Department signature
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