Lectures: 

Lecture 1: Axioms of Probability 

Lecture 2: The Probability Space

Lecture 3: Conditional Probability

Lecture 4: Bayes Rule

Lecture 5: Random Variables

Lecture 6: Probability Distribution

Lecture 7: Distribution Functions

Lecture 8: Expectation

Lecture 9: Special Functions

Lecture 10: Characteristic Functions

Lecture 11: Characteristic Functions (Continued)

Lecture 12: Concentration Inequalities

Lecture 13: Convergence of Random Variables

Lecture 14: Convergence of Random Variables (Continued)

Lecture 15: Central Limit Theorem

Lecture 16: Key Concepts in Statistics

Lecture 17: Parameter Estimation

Lecture 18: Maximum Likelihood Estimator

Lecture 19: Cramer-Rao Bound

Lecture 20: Estimation Error

Lecture 21: Methods for MLE

Lecture 22: Nonparametric Estimation

Lecture 23: Plug-in Estimators

Lecture 24: Bootstrap

Lecture 25: Hypothesis Testing