Part I: The Fundamentals (M-I-T)

Lecture 1: Probability Models and Axioms (M-I-T)

Lecture 1 Supplement: Mathematical Background (M-I-T)

Lecture 2: Conditioning and Bayes' Rule (M-I-T)

Lecture 3: Independence (M-I-T)

Lecture 4: Counting (M-I-T)

Lecture 5: Discrete Random Variables Part I (M-I-T)

Lecture 6: Discrete Random Variables Part II (M-I-T)

Lecture 7: Discrete Random Variables Part III (M-I-T)

Lecture 8: Continuous Random Variables Part I (M-I-T)

Lecture 9: Continuous Random Variables Part II (M-I-T)

Lecture 10: Continuous Random Variables Part III (M-I-T)

Lecture 11: Derived Distributions (M-I-T)

Lecture 12: Sum of Independent R.V.s. Covariance and Correlation (M-I-T)

Lecture 13: Conditional Expectation & Variance Revisited; Sum of a Random Number of Independent R.V.s (M-I-T)