Lecture 1 (Introduction)
Lecture 2 (Permutation & Combinations) (slides & video)
Lecture 3 (Combinatorial Proofs) (slides & video)
Lecture 4 (Induction/Multinomial Theorem/Principle of Inclusion-Exclusion) (slides & video)
Lecture 5 (Integer Solutions & Axioms of Probability) (slides & video)
Lecture 6 (Sample Spaces with Equally Likely Outcomes) (slides & video)
Lecture 7 (Examples) (slides)
Lecture 8 (Inclusion-Exclusion Inequalities) (slides)
Lecture 9 (Conditional Probability I) (slides)
Lecture 10 (Conditional Probability II) (slides)
Lecture 11 (Bayes' Theorem) (slides & video)
Lecture 12 (Independence I) (slides)
Lecture 13 (Independence II) (slides)
Lecture 14 (Conditional Probability as a Probability) (slides)
Lecture 15 (Random Variables) (slides)
Lecture 16 (PMF's, CDF's and Expected Value) (slides)
Lecture 17 (Expectation of Functions and Variance) (slides)
Lecture 18 (Poisson & Geometric Random Variables) (slides)
Lecture 19 (Negative Binomial & Hypergeometric Random Variables/Linearity of Expectation) (slides)
Lecture 20 (Continuous Random Variables) (slides)
Lecture 21 (Normal and Uniform Distribution) (slides)
Lecture 22 (Exponential Distribution) (slides)
Lecture 23 (Joint Distributions) (slides)
Lecture 24 (Independent Random Variables) (slides)
Lecture 25 (Sums of Random Variables) (slides & video)
Lecture 26 (Sums of Gaussians) (slides)
Lecture 27 (The Gamma Distribution) (slides)
Lecture 28 (Review) (slides)
Lecture 29 (Conditional Density) (slides)
Lecture 30 (Expectation of Functions of Many Random Variables) (slides)
Lecture 31 (Applications of Expectations) (slides)
Lecture 32 (Covariance) (slides)
Lecture 33 (Conditional Expectation) (slides)
Lecture 34 (Moment Generating Functions) (slides)
Lecture 35 (Review of Recent Material) (slides)
Lecture 36 (Central Limit Theorem and the Law of Large Numbers) (slides)
Lecture 37 (Review) (slides)