Lecture Notes

These slides can also be found in the Video Lectures section, in the Lecture Slides tab associated with each video.

LEC #	LECTURE NOTES
1	Probability Models and Axioms (PDF)
2	Conditioning and Bayes’ Rule (PDF)
3	Independence (PDF)
4	Counting (PDF)
5	Discrete Random Variables; Probability Mass Functions; Expectations (PDF)
6	Discrete Random Variable Examples; Joint PMFs (PDF)
7	Multiple Discrete Random Variables: Expectations, Conditioning, Independence (PDF)
8	Continuous Random Variables (PDF)
9	Multiple Continuous Random Variables (PDF)
10	Continuous Bayes’ Rule; Derived Distributions (PDF)
11	Derived Distributions; Convolution; Covariance and Correlation (PDF)
12	Iterated Expectations; Sum of a Random Number of Random Variables (PDF)
13	Bernoulli Process (PDF)
14	Poisson Process - I (PDF)
15	Poisson Process - II (PDF)
16	Markov Chains - I (PDF)
17	Markov Chains - II (PDF)
18	Markov Chains - III (PDF)
19	Weak Law of Large Numbers (PDF)
20	Central Limit Theorem (PDF)
21	Bayesian Statistical Inference - I (PDF)
22	Bayesian Statistical Inference - II (PDF)
23	Classical Statistical Inference - I (PDF)
24	Classical Inference - II (PDF)
25	Classical Inference - III; Course Overview (PDF)

Course Info