| 1 | Probability Models and Axioms (PDF) |
| 2 | Conditioning and Bayes' Rule (PDF) |
| 3 | Independence (PDF) |
| 4 | Counting (PDF) |
| 5 | Discrete Random Variables; Probability Mass Functions; Expectations (PDF) |
| 6 | Discrete Random Variable Examples; Joint PMFs (PDF) |
| 7 | Multiple Discrete Random Variables: Expectations, Conditioning, Independence (PDF) |
| 8 | Continuous Random Variables (PDF) |
| 9 | Multiple Continuous Random Variables (PDF) |
| 10 | Continuous Bayes' Rule; Derived Distributions (PDF) |
| 11 | Derived Distributions; Convolution; Covariance and Correlation (PDF) |
| 12 | Iterated Expectations; Sum of a Random Number of Random Variables (PDF) |
| 13 | Bernoulli Process (PDF) |
| 14 | Poisson Process - I (PDF) |
| 15 | Poisson Process - II (PDF) |
| 16 | Markov Chains - I (PDF) |
| 17 | Markov Chains - II (PDF) |
| 18 | Markov Chains - III (PDF) |
| 19 | Weak Law of Large Numbers (PDF) |
| 20 | Central Limit Theorem (PDF) |
| 21 | Bayesian Statistical Inference - I (PDF) |
| 22 | Bayesian Statistical Inference - II (PDF) |
| 23 | Classical Statistical Inference - I (PDF) |
| 24 | Classical Inference - II (PDF) |
| 25 | Classical Inference - III; Course Overview (PDF) |
