Lectures

Tuesdays & Thursdays, 2:30-3:50, in CIT 368

Primary readings below refer to sections in the course textbook, Introduction to Probability (second edition) by Bertsekas and Tsitsiklis. For material in the first half of the course, Pitman's Probability is recommended as a secondary reference.


# Date Topics Primary Reading Materials
0 Sep 6 Course Overview 1.0 slides
1 Sep 11 Sets and Events, Counting 1.1-1.2, 1.6 slides
2 Sep 13 Conditioning, Bayes' Rule 1.3-1.4 slides
3 Sep 18 Conditioning, Independence 1.4-1.5 slides
4 Sep 20 Independence, Classification, Discrete Random Variables 1.4-1.5, 2.1 slides
5 Sep 25 Functions of Random Variables, Expectations 2.2-2.4 slides
6 Sep 27 Multiple Random Variables, Independence 2.5-2.7 slides, Naive Bayes notes
7 Oct 2 Conditional Expectation, Variance 2.4, 2.6 slides
8 Oct 4 Cumulative Distributions 3.2 slides
9 Oct 9 Continuous Probability Densities, Gaussian Distributions 3.1-3.3 slides
10 Oct 11 Continuous Conditioning, Bayes' Rule 3.4-3.6 slides
11 Oct 16 Conditional Densities, Derived Distributions 4.1, 4.5 slides
12 Oct 18 Derived Distributions, Sums of Random Variables 4.1, 4.3 slides
13 Oct 23 Covariance, Bivariate Normal Distributions Supplement, 4.2 see above slides
14 Oct 25 Multivariate Normals, Linear Regression, Rare Events Supplement, 9.2, 5.1 slides
M Oct 30 MIDTERM EXAM
15 Nov 1 Law of Large Numbers, Central Limit Theorem 5.2-5.4 slides
16 Nov 6 Monte Carlo Methods slides
17 Nov 8 Markov Chain Foundations 7.1 slides
18 Nov 13 Markov Chain Equilibria 7.2-7.3 slides
19 Nov 15 Markov Chain Absorption, Bayesian Hypothesis Tests 7.4, 8.2 slides
20 Nov 20 Bayesian & Frequentist Hypothesis Tests 8.2, 9.3 slides
21 Nov 27 Maximum Likelihood Parameter Estimation 9.1 slides
22 Nov 29 Bayesian Parameter Estimation 8.1, 8.3 slides
23 Dec 4 Significance Tests, Confidence Intervals, Review 9.4 slides
F Dec 17 FINAL EXAM (9:00-12:00pm, TBA)