Pattern Recognition

Lecture Curricula:

Introduction

Probability Review

Bayesian Decision Theory 1

Bayesian Decision Theory 2

Maximum Likelihood and K Nearest Neighbors Estimation

Kernel Density Estimation

Linear Discriminant Functions, Perceptron

Multilayer Perceptron

Kernel Methods

Support Vector Machines

Ensemble Methods

Clustering Methods

Feature Selection and Performance Estimation

Textbooks and references:

1. R. O. Duda, E. Hart, D. G. Stork, “Pattern Classification”, John Wiley & Sons, 2001

2. S. Theodoridis, K. Koutroumbas, “Pattern Recognition”, 4^th edition, Academic Press, 2008.

3. C.M. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006

4. Murphy, “Machine Learning: A Probabilistic Perspective”, The MIT Press, 2012

5. I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, The MIT Press, 2016

6. Convolutional Neural Networks for Visual Recognition, http://cs231n.stanford.edu/, 2019