Reading assignments are from the course textbook: Bertsekas, Dimitri, and John Tsitsiklis. Introduction to Probability. 2nd ed. Athena Scientific, 2008. ISBN: 978188652923.

1 Probability models and axioms Sections 1.1–1.2
2 Conditioning and Bayes' rule Sections 1.3–1.4
3 Independence Section 1.5
4 Counting Section 1.6
5 Discrete random variables; probability mass functions; expectations Sections 2.1–2.4
6 Discrete random variable examples; joint PMFs Sections 2.4–2.5
7 Multiple discrete random variables: expectations, conditioning, independence Sections 2.6–2.7
8 Continuous random variables Sections 3.1–3.3
9 Multiple continuous random variables Sections 3.4–3.5
10 Continuous Bayes rule; derived distributions Sections 3.6; 4.1
11 Derived distributions; convolution; covariance and correlation Sections 4.1–4.2
12 Iterated expectations; sum of a random number of random variables Sections 4.3; 4.5
13 Bernoulli process Section 6.1
14 Poisson process – I Section 6.2
15 Poisson process – II Section 6.2
16 Markov chains – I Sections 7.1–7.2
17 Markov chains – II Section 7.3
18 Markov chains – III Section 7.3
19 Weak law of large numbers Sections 5.1–5.3
20 Central limit theorem Section 5.4
21 Bayesian statistical inference – I Sections 8.1–8.2
22 Bayesian statistical inference – II Sections 8.3–8.4
23 Classical statistical inference – I Section 9.1
24 Classical inference – II Sections 9.1–9.4
25 Classical inference – III; course overview Sections 9.1–9.4