EM Algorithm

05-28

Bayes Rule, Prior and Posterior

05-27

Kernel Tricks

05-27

Bias and Variance

05-27

Radial Basis Function, RBF network

05-27

SVM, Slack Variable and Kernel

05-26

Naive Bayes

05-26

Discriminative and Generative Models

05-25

Sklearn Split Train and Test

04-30

Gaussian Mixed Model(GMM) and EM algorithm

04-14