Transcript Machine learning and Neural Networks

DEPARTMENT of
INFORMATION
TECHNOLOGIES
CEN 592
PATTERN RECOGNITION
2011-2012 Spring Term
Assoc. Prof. Dr. Abdülhamit Subaşı
[email protected]
 Office Hour: Open Door Policy
 Class Schedule:Monday 17:00-19:45
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.
Textbooks
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.
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
Grading
Paper Presentation
Research
Final Exam (Implementation )
30%
30%
40%
Minimum 20 pages word document (12 pnt) and related PPT (40 ppt) presentation
Research Topics:
 Biometrics authentication systems
 Face recognition
 Speech Recognition
 Biomedical Signal Recognition

Paper presentation
Presentation
 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, CART, ID3, C4.5, (Non-metric Methods)
 Principal Component Analysis Networks (PCA, KPCA, MPCA, ICA, LDA)
 Support Vector Machines (SVM), FSVM, WSVM
 Fuzzy Logic and Neurofuzzy Systems -
 Artificial Neural Networks
 Evaluation Hypotheses
 Bayesian Learning
 Computational Learning Theory
 Reinforcement Learning
 Genetic Algorithms , Particle Swarm optimization