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Welcome to Statistical Machine Learning Course
What Is Statistical Machine Learning?
Understanding the Covariance Matrix
Maximum Likelihood Estimation (MLE)
Mahalanobis Distance
Naive Bayes Classification
Bayesian Decision Theory
Linear models
Feature Maps: Bridging to Kernel Methods
The Kernel Method (Kernel Trick)
Kernel Regression: From Linear to Nonlinear Modeling
Gaussian Mixture Models, Part 1
Gaussian Mixture Model, Part 2
Principal Component Analysis, Part 1
Principal Component Analysis, Part 2
Hidden Markov Models
Appendix: Mathematics and Machine Learning
Appendix: Bayes Decision Theory — Discrete Features
Appendix: The Moore-Penrose Pseudoinverse
Appendix: Direct Maximization of the GMM Log-Likelihood
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