| Date | Sections | Topics |
|---|---|---|
| 9/4 | 1.1--1.5 | Combinatorics |
| 9/7 | 1.6--2.3 | Indistinguishable objects; axioms of probability |
| 9/11 | 2.4, 2.5 | Inclusion/exclusion formula; equally likely outcomes |
| 9/14 | 2.5 | More examples; Stirling's approximation |
| 9/18 | 3.1--3.3 | Conditional probability and Bayes' formula |
| 9/21 | 3.4 | Independent events |
| 9/25 | 3.4, 3.5 | Repeated independent trials |
| 9/28 | 4.1--4.2 | Random variables and distribution functions |
| 10/2 | 4.3--4.5 | Expectation and variance of discrete random variables |
| 10/5 | 4.6, 4.8.1 | Bernoulli, binomial, and geometric random variables |
| 10/9 | EXAM 1 | All material covered through lecture of 10/2 |
| 10/12 | 4.7, 4.9.2, 4.9.3 | Poisson, negative binomial, and hypergeometric random variables |
| 10/16 | 5.1, 5.2 | Continuous random variables; expectation, variance |
| 10/19 | 5.3--5.5 | Uniform, exponential, and normal random variables |
| 10/23 | 5.4.1 | Normal approximation to binomial random variables |
| 10/26 | 5.6.1, 5.7 | Gamma random variable; functions of a random variable |
| 10/30 | 6.1 | Joint distributions of several random variables |
| 11/2 | 6.2 | Independent random variables |
| 11/6 | 6.3 | Sums of independent random variables |
| 11/9 | 7.1--7.3 | Linearity of expectation |
| 11/13 | EXAM 2 | All material covered through lecture of 11/6 |
| 11/16 | 7.4 | Covariance and correlation |
| 11/21 | 6.4, 6.5 | Conditional distributions |
| 11/27 | 7.5 | Conditional expectation |
| 11/30 | 7.7 | Moment generating functions |
| 12/4 | 8.1--8.2 | Markov and Chebyshev inequalities; weak law of large numbers |
| 12/7 | 8.3 | The central limit theorem |
| 12/11 | 8.3 | Proof of the central limit theorem; examples. |
| 12/18 | FINAL EXAM | 8:00 A.M.--11:00 A.M. |