The readings in the right-hand column are either lecture notes or sections in Grinstead and Snell (GS).
| Date | Topic | Reading |
| Sep 27 | Introduction | Mathematical preliminaries, GS 1.2 |
| Sep 29 | Probability spaces | Probability spaces, GS 3.1, 3.2 |
| Oct 4 | Multiple events | |
| Oct 6 | Conditional probability | Multiple events, conditioning, and independence I, GS 4.1 |
| Oct 11 | Conditional probability, Independence | Multiple events, conditioning, and independence II |
| Oct 13 | Random variables | Random variables, expectation, and variance I |
| Oct 18 | Linearity of expectation | GS 6.1, 6.2 |
| Oct 20 | Variance | Random variables, expectation, and variance II |
| Oct 25 | Randomized algorithms: sorting and selection | Randomized algorithms I |
| Oct 27 | Midterm 1 | |
| Nov 1 | Clustering and graph cuts | |
| Nov 3 | Hashing | Randomized algorithms II |
| Nov 8 | Information retrieval | Randomized algorithms III |
| Nov 10 | Random generation | Random generation I |
| Nov 15 | The binomial in sampling and hypothesis testing | Random generation II |
| Nov 17 | Midterm 2 | Useful facts for the midterm |
| Nov 22 | The central limit theorem | |
| Nov 24 | Sampling and hypothesis testing, cont'd | Sampling, hypothesis testing, and the central limit theorem |
| Nov 29 | Nearest neighbor classification and decision trees | |
| Dec 1 | Linear classifiers | Machine learning |