Lecture Notes
- Lecture 1 – Sample Spaces
- Lecture 2 – Equally Likely Outcomes
- Lecture 3 – Binomial Coefficients
- Lecture 4 – Identities and Probability Measures
- Lecture 5 – Inclusion-exclusion
- Lecture 6 – Conditional Probability
- Lecture 7 – Bayes’ Theorem
- Lecture 8 – Independent Events
- Lecture 9 – Random Variables
- Lecture 10 – Binomial Random Variables
- Lecture 11 – Discrete Distributions
- Lecture 12 – Change of Variables and Independence
- Lecture 13 – Connections: Binomial and Hypergeometric
- Lecture 14 – Expectation
- Lecture 15 – Geometric and Negative Binomial Distributions
- Lecture 16 – Variance
- Lecture 17 – The Poisson Distribution
- Lecture 18 – Continuous Random Variables
- Lecture 19 – The Normal Distribution
- Lecture 20 – The Exponential Distribution
- Lecture 21 – Moments
- Lecture 22 – Properties of Moment Generating Functions
- Lecture 23 – Joint Distributions
- Lecture 24 – Independence and Conditional Distributions
- Lecture 25 – Expectations and Transformations
- Lecture 26 – Covariance
- Lecture 27 – The Gamma Distribution
- Lecture 28 – Some Inequalities
- Lecture 29 – Sums of Independent Random Variables
- Lecture 30 – The Central Limit Theorem
- Lecture 31 – Central Limit Theorem: Examples
- Lecture 32 – Chi-square Distribution