Machine learning and Neural Networks
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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