Lecture 01: Review on probability and statistics
Lecture 02: Nonparametric density estimation
Lecture 03: Nonparametric regression
Lecture 04: Linear regression in low- and high-dimensions
Lecture 06: SVM, PCA, and Kernel methods
Lecture 07: Model selection and prediction
Lecture 09: Monte Carlo inferences
Lecture 09-1: Hamiltonian Monte Carlo
Lecture 10: The bootstrap method
Lecture 11: Graphical and network models
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