1. CEN 592 PATTERN RECOGNITION 2011-2012 Spring Term CEN 592 PATTERN RECOGNITION 2011-2012 Spring Term DEPARTMENT of INFORMATION TECHNOLOGIES Assoc. Prof. Dr. Abdülhamit Subaşı asubasi@ibu.edu.ba

2. Office Hour: Open Door Policy Class Schedule:Monday 17:00-19:45

3. Course Objectives Presenting the key algorithms and theory that form the core of pattern recognition. Characterizing and recognizing patterns and features of interest in numerical data. decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering.

4. 1. S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras, Introduction to Pattern Recognition A MATLAB® Approach, Academic Press, Elsevier Inc. 2010. 2. R. O. Duda, P. E. Hart and D. Stork, Pattern Classification, 2nd. Edition, John Wiley & Sons, 2002. 3. K C. Bishop, Pattern Recognition and Machine Learning, Springer 2006. 4. L. I. Kuncheva, Combining Pattern Classifiers, Methods and Algorithms, John Wiley & Sons, Inc., 2004. 5. S. Theodoridis, K. Koutroumbas, Pattern Recognition & MATLAB Intro, Elsevier, 2010. 6. Menahem Friedman, Abraham Kandel, Introduction to Pattern Recognition, Statistical, Structural, Neural and Fuzzy Logic Approaches, World Scientific Publishing Company, 1999. 7. S. K. Pal, A. Pal, Pattern Recognition, From Classical to Modern Approaches, World Scientific Publishing Company, 2001. 8. A. R. Webb, Statistical Pattern Recognition, Second Edition, John Wiley & Sons, Ltd., 2002. Textbooks

5. Brief Contents Linear and Quadratic Discriminants, Fisher Discriminant Template-based Recognition, Feature Extraction Training Methods, Maximum Likelihood and Bayesian Parameter Estimation Bayesian Learning Linear Discriminant/Perceptron Learning, Optimization by Gradient Descent Artificial Neural Networks Support Vector Machines K-Nearest-Neighbor Classification Non-parametric Classification, Density Estimation, Parzen Estimation Unsupervised Learning, Clustering, Vector Quantization, K-means, C means Mixture Modeling, Expectation-Maximization Hidden Markov Models, Viterbi Algorithm, Baum-Welch Algorithm Decision Tree Learning Evaluation Hypotheses Computational Learning Theory Reinforcement Learning Genetic Algorithms, Particle Swarm optimization

6. Grading Paper Presentation 30% Research30% Final Exam (Implementation ) 40% Minimum 20 pages word document (12 pnt) and related PPT (40 ppt) presentation

7. Research Topics: Biometrics authentication systems Biometrics authentication systems Face recognition Face recognition Speech Recognition Speech Recognition Biomedical Signal Recognition Biomedical Signal Recognition Paper presentation

8. Presentation K-Nearest-Neighbor Classification K-Nearest-Neighbor Classification Non-parametric Classification, Density Estimation, Parzen Estimation Non-parametric Classification, Density Estimation, Parzen Estimation Unsupervised Learning, Clustering, Vector Quantization, K-means, C means Unsupervised Learning, Clustering, Vector Quantization, K-means, C means Mixture Modeling, Expectation-Maximization Mixture Modeling, Expectation-Maximization Hidden Markov Models, Viterbi Algorithm, Baum-Welch Algorithm Hidden Markov Models, Viterbi Algorithm, Baum-Welch Algorithm Decision Tree Learning, CART, ID3, C4.5, (Non-metric Methods) Decision Tree Learning, CART, ID3, C4.5, (Non-metric Methods) Principal Component Analysis Networks (PCA, KPCA, MPCA, ICA, LDA) Principal Component Analysis Networks (PCA, KPCA, MPCA, ICA, LDA) Support Vector Machines (SVM), FSVM, WSVM Support Vector Machines (SVM), FSVM, WSVM Fuzzy Logic and Neurofuzzy Systems - Fuzzy Logic and Neurofuzzy Systems - Artificial Neural Networks Artificial Neural Networks Evaluation Hypotheses Evaluation Hypotheses Bayesian Learning Bayesian Learning Computational Learning Theory Computational Learning Theory Reinforcement Learning Reinforcement Learning Genetic Algorithms, Particle Swarm optimization Genetic Algorithms, Particle Swarm optimization