Course Description:
Covers basic concepts of pattern recognition systems, application examples, PDF estimation, maximum likelihood estimation, Bayesian estimation, KNN estimation, expectation maximization algorithm, feature reduction, supervised classification, Bayesian classification, discriminant functions, classifier combination, Markov random fields, Artificial neural networks, support vector machines, deep learning.
Textbook(s) and/or Other Required Materials:

  1. Duda, Heart and Storck, Pattern classification, 2nd edition, 2000
  2. Cristopher M. Bishop, Pattern recognition and machine learning, 2006   

Major Topics covered and schedule in weeks:
Recognition systems                                                                           2
Statistical estimation theory                                                               2
Supervised Classification                                                                   2                     
Artificial neural networks and deep learning                                               3                     

ملحقات المادة الدراسية