Rzeszów University of Technology
Electrical and Computer Engineering
The Faculty of:
Field of study:
Computer Science
Speciality:
Information systems
Study degree (BSc, MSc):
MSc
COURSE UNIT DESCRIPTION
Artificial Intelligence
Course title:
Lecturer responsible for course: Jacek Kluska, PhD, Assoc. Prof.
Contacts: phone: +48 17 8651247
e-mail: jacklu@prz.edu.pl
Department : Computer Science and Automatic Control
Type of classes
Semester
Weekly load
6
5
L
Lectures
C
Theoretical
Classes
Lb
Laboratory
45
30
P
Project
Number of
ECTS
credits
6
Course description
Lecture:
Description of uncertainty. Fuziness vs. probability.Multivalued logics. Knowledge-bases and inference
methods. Generalized expert system design. Fuzzy expert systems. Artificial neural networks. Classification.
Linear separability. Convergence of the perceptron learning. Multilayer networks. 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. Counterpropagation networks. CP-networks. Unsupervised learning in the Hopfield networks. k-nearest neighbors
algorithm. Naive Bayes classifier. Decision trees and families of classifiers. Gene Expression Programming and
its applications. Support Vector Machines. Classification vs. regression. Optimal separating hyperplane. Kernel
functions. SMO algorithm. Applications of SVM in medical diagnosis.
Classes:
Laboratory:
Navigation system for a mobile as an experts system. Basic learning algorithms of the neural networks.
Backpropagation. Optical character recognition. Hopfield networks. Classification methods using SVM, k-NN,
neural networks (RBF), decision trees and Gene Expression Programming. Crossvalidation. Application of
classifiers to medical diagnosis.
Project:
Objectives of the course
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.
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7.
8.
Ferreira C., Gene expression programming. Springer-Verlag, 2006.
Gunn S, Support Vector Machines for Classification and Regression, University of Southampton, 1998
Haykin S., Neural Networks – a Comprehensive Foundation, Macmillan College Publishing Company,
New York, 1994.
Kluska J., Analytical methods in fuzzy modeling and control. Springer-Verlag, 2009.
Kosko B., Neural Networks and Fuzzy Systems. A Dynamical Systems Approach to Machine Intelligence.
Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1992.
Matlab User’s Guide, The MathWorks Inc., 1999.
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.
Rifkin R., Everything Old Is New Again: A Fresh Look at Historical Approaches in Machine Learning,
PhD Thesis, MIT, 2002.
Lecturer signature
Head of Department signature
Dean signature