I use these notes as a way to make sure I understand the basic concepts of each topic. I may use them as supplementary materials of graduate courses that I will be teaching. Comments and suggestions are welcome.

A short note on quantile classifiers.

A short note on the algorithmic fairness.

A short note on the kernel VC-type condition.

A short note on conditional models, robustness, and missingness.

A short note on the median-of-means estimator.

A short note on the Bernstein-von Mises theorem under infinite dimensions.

A short note on the causality and its minimax framework.

A short note on the linear form of the Cox model estimator.

A short note on the empirical likelihood and calibration.

A short note on the Frequentist consistency of a nonparametric Bayes.

A short note on the experimental design.

A note on the semi-parametric estimators.

• This one is longer (about 40 pages) and new contents may be added.

A short note on the adjustment sets.

A short note on the variational inference.

A short note on the Rasch model and latent trait model.

A short note on the mixture of experts.

A short note on the constrained nonparametric quantile regression.

A short note on the effect mediation.

A short note on the coarsening at random.

A short note on the $$L_\infty$$ concentration of the KDE.