This thesis presents detailed discussions of selected topics in statistical machine learning theory. I analyze and reproduce classical machine learning methods, Discriminant Analysis, EM algorithm, Principle Component Analysis, and Kernel Methods as well as their derivatives. I also present experiment and simulation of the methods for illustration of their properties. Finally, I present a project of multi-class handwritten digit images classification applying all the methods discussed and compare the corresponding classification performances.
