
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)



Hypothesis Testing




Lecture 11  Hypothesis Testing
92:00




Annotated Notes Lecture 11  Hypothesis Testing
(24 pages)




Lecture 12  Hypothesis Testing (Vishal Sir)




Annotated Notes Lecture 12  Hypothesis Testing2
(46 pages)




Lecture 13  Hypothesis Testing (Vishal Sir)




Annotated Notes Lecture 13  Hypothesis Testing3
(35 pages)

