| Date | Sections | Topics |
|---|---|---|
| 9/05 | 2.1 – 2.2, 1.1 – 1.3 | Course intro. Sample spaces and intro to counting |
| 9/09 | 2.3, 1.1 – 1.4 | Product principle for counting lists, counting subsets. Events |
| 9/12 | 2.3 – 2.5 | Probability functions; equally likely outcomes |
| 9/16 | 1.4 – 1.5, 2.4 | Probability calculation examples. Binomial and multinomial coefficients. Principle of inclusion-exclusion |
| 9/19 | 3.1 – 3.3 | Conditional probability and Bayes' formula |
| 9/23 | 3.4 – 3.5 | Independent events and more conditional probability |
| 9/26 | 3.4 – 3.5 | Conditional probability and independence. More examples |
| 9/30 | 4.1 – 4.3 | Random variables, distribution functions and expectation |
| 10/03 | 4.4 – 4.5, 4.9 | Expectation (continued) and variance of discrete random variables |
| 10/07 | 4.6 – 4.7 | Bernoulli, binomial and poisson random variables |
| 10/10 | 4.8 | More discrete random variables |
| 10/14 | EXAM 1 | All material covered through lecture of 10/07 |
| 10/17 | 5.1 – 5.2 | Continuous random variables; expectation, variance |
| 10/21 | 5.3 – 5.5 | Uniform, exponential, and normal random variables |
| 10/24 | 5.4.1;5.6.1 | Normal approximation to binomial random variables; Gamma random variables |
| 10/28 | 5.7 | Functions of a random variable |
| 10/31 | 6.1 | Joint distributions of several random variables |
| 11/04 | 6.2 | Independent random variables |
| 11/07 | 6.3 | Sums of independent random variables |
| 11/11 | 7.1 – 7.2 | Linearity of expectation |
| 11/14 | 7.4 | Covariance and correlation |
| 11/18 | EXAM 2 | All material covered through lecture of 11/11 |
| 11/21 | 6.4, 6.5 | Conditional distributions |
| 11/25 | 7.5 | Conditional expectation |
| 11/26 (Univ. follows Thurs. schedule | 7.7 | Moment generating functions |
| 12/02 | 8.1 – 8.2 | Markov and Chebyshev inequalities; weak law of large numbers |
| 12/05 | 8.3 | The central limit theorem |
| 12/9 | 8.3 | Proof of the central limit theorem; examples. |
| 12/20 | FINAL EXAM | 4:00 – 7:00 P.M. |