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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 Dean signature