Lecture Notes

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

All lecture slides as one file (PDF - 5.7MB)

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)