Topic 1: Classical nonparametric approaches

Lecture 00: Review on probability and statistics

Lecture 01: Robust two-sample test

Lecture 02: CDF and EDF

Lecture 03: Permutation test

Lecture 04: Contingency table

Lecture 05: Survival analysis and missing data

Topic 2: Nonparametric smoothing approaches

Lecture 06: Density Estimation – histogram and KDE

Lecture 07: Density Estimation – kNN and basis approach

Lecture 08: Density Estimation – parametric approach

Lecture 09: Regression Analysis – regressogram and kernel regression

Lecture 10: Regression Analysis – basis approach and regression tree

Lecture 11: Regression Analysis – penalized approach

Lecture 12: Classification – density estimation and regression method

Lecture 13: Classification – multivariate method

Topic 3: Statistical learning theory

Lecture 14: Normal means problem

Lecture 15: Concentration inequalities

Lecture 16: Empirical risk minimization

Lecture 17: Model selection