
Probability Module 1




Lecture 1A. Probability Definition, Sample Space and Events
28:00




Lecture 1B. Inclusion Exclusion Principle, Demorgan's Law
37:00




Lecture 1 Annotated Notes
(40 pages)




Lecture 2A. Conditional Probability Introduction
34:00




Lecture 2B. Conditional Probability Examples
26:00




Lecture 2C. Introduction to Tree Diagram
32:00




Annotated Notes Lecture 2 Conditional Probability
(70 pages)




Lecture 3A. Total Probability
36:00




Lecture: GATE PYQs Tree Method
95:00




Annotated Notes Lecture GATE PYQs Tree Method
(35 pages)




Lecture3B. Conditional Probability Examples
33:00




Annotated Notes Lecture 3A3B
(55 pages)




Lecture 3C: GATE PYQs and Bayes Theorem




Annotated Notes Lecture 3C: GATE PYQs and Bayes Theorem
(48 pages)




[Optional] My LinkedIn Interview
49:00




Annotated Notes [Optional] My Linkedin Interview
(28 pages)




Lecture 4A. independence of events
43:00




Lecture 4B. Conditional Independence
32:00




Lecture 4C. Independence Does not imply Conditional Independence
22:00




Annotated Notes Lecture 4C. Independence Does not imply Conditional Independence
(22 pages)




Lecture 4d: Random Variables Introduction




Annotated Notes Lecture 4d: Random Variables Introduction
(46 pages)




Lecture 5A. Practice Questions on Random Variables
29:00




Annotated Notes Lecture 5a. Practice Questions on Random Variables
(20 pages)




Lecture 5B. Practice Questions on Random Variables
67:00




Annotated Notes Lecture 5b. Practice Questions on Random Variables
(46 pages)




Lecture 6A. Types of Random Variables
9:00




Lecture 6B. Probability Mass Function Questions Part 1
20:00




Lecture 6C. Probability Mass Function Questions Part 2
15:00




Lecture 6 Annotated Notes
(39 pages)




Lecture 7A. Expectation of Discrete Random Variable
19:00




Lecture 7B Expectation vs Average
40:00




Lecture 7C. Expectation as center of mass
8:00




Lecture 7D. MIT Question and GATE 2021 Question
13:00




Lecture 7E. Expectation of Random Variable which is a Function of Random Variable
18:00




Lecture 7 Annotated Notes
(70 pages)




Lecture 7F. Recursive Ways to find Expectation




Annotated Notes Lecture 7F Recursive Ways to find Expectation
(35 pages)




Lecture 8A. Question number 8
21:00




Lecture 8B. Question number 9, 10, 11
17:00




Lecture 8C. Question number 12, 13
30:00




Lecture 8 Annotated Notes Expectation Tree Method
(37 pages)




Lecture 9A. Cumulative Distribution Function
31:00




Lecture 9B. Variance Intuition and Formula
52:00




Lecture 9C. Variance Main Formula and Questions
38:00




Lecture 9D. Variance Questions
45:00




Annotated Notes Lecture 9 CDF and Variance
(85 pages)




10A. Discrete random variable (Bernoulli and Binomial RVs)
77:00




Lecture 10B. MIT Question on Binomial RV
16:00




Lecture 10C. Optional and Skip  Question on Plot of PMF
19:00




Lecture 10D. Poisson Random Variable
28:00




Lecture 10E. Discrete Uniform Random Variable Introduction
15:00




Annotated Notes Lecture 10 DRVs
(79 pages)




Lecture 11A. Introduction to Continuous Distributions  Intuition about PDF
32:00




Lecture 11B. Continuous Uniform Distribution
25:00




Lecture 11C. Normal Distribution
43:00




Lecture 11D. Normal Distribution  2
17:00




Lecture 11E. Exponential Distribution
18:00




Annotated Notes Lecture 11 Continuous Random Variable
(83 pages)




Lecture 12. Statistics  Mean Mode Median
35:00




Annotated Notes Lecture 12 Mean Mode Median
(20 pages)



Probability Module 2




Lecture 1: Joint Probability Mass Function




Annotated Notes Lecture 1 Module 2 Join PMFs
(78 pages)




Lecture 2: Introduction to Conditional Expectation




Annotated Notes Lecture 1 Module 2 Conditional Expectation
(59 pages)




Lecture 3: Conditional Expectation, Total Expectation




Annotated Notes Lecture 3 Module 2 Law of Total Expectation
(81 pages)




Lecture 4: Continuous Random Variable, CDFs, and Expectation




Annotated Notes Lecture 4 Module 2 Continuous Random Variable, CDFs, and Expectation
(111 pages)




Lecture 5: Joint PDF, CDF, Conditional PDF, Conditional Expectation of Joint distribution




Annotated Notes Lecture 5 Module 2 Joint PDF, Joint CDFs
(90 pages)




Practice Set on Continuous Distributions
(51 pages)




Lecture 6: (Solution of Practice Set) More Questions on Continuous RVs




Annotated Notes Lecture 6 Module 2 Conditional Expectation in Continuous Case
(67 pages)




Lecture 7: Covariance




Annotated Notes Lecture 7 Module 2 Covariance
(103 pages)




Lecture 8: Covariance Questions and Covariance Matrix




Annotated Notes Lecture 8 Module 2 Covariance Questions and Covariance Matrix
(75 pages)




Lecture 9: Solution of Weekly Quiz 10 on Conditional Expectation, Joint PMF, Joint CDF, Covariance, Covariance Matrix




Lecture 10: Correlation and Many Questions on Correlation




Annotated Notes Lecture 10 Module 2 Correlation
(60 pages)

