This is a course in statistical inference, mostly parametric.

The source book is Larry Wasserman's "All of Statistics".

Lectures recording playlist: https://youtube.com/playlist?list=PLJR10EXrBaAvz-e2QXiVHs7Cck5tamJVr

Outline:

Lecture, slides link	Youtube recording link
1) Course & parametric inference overview	see playlist
2) Empirical CDF, Bootstrap	https://youtu.be/LNcU2YN9aIw
3) Maximum Likelihood Estimation (MLE), Method of Moments (MOM)	https://youtu.be/K6NvZi8uYZE
4) Delta method, exponential families	https://youtu.be/mVQipbzmDaE
5) Hypothesis testing 1: overview, one- vs two-sided tests, p-values, Wald's test, chi-squared	https://youtu.be/lT4oIafAYPI
6) Hypothesis testing 2: permutation test, likelihood ratio, multiple testing, goodness-of-fit, t-test	https://youtu.be/4bE_7pQU2J8
7) Bayesian inference	https://youtu.be/B-ztie8jykw
8) Regression linear and logistic	https://youtu.be/-dEn7lUbXPs
9) Inference about independence	https://youtu.be/nLY6Lq6pvtk
10) Causal inference	https://youtu.be/-ddX79K8aM4
11) Nonparametric methods	https://youtu.be/6yqRzyZNLys