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

Useful facts for the final