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Probability Module 1
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Lecture 1A. Probability Definition, Sample Space and Events
28:00
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Lecture 1B. Inclusion Exclusion Principle, Demorgan's Law
37:00
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Lecture 1 Annotated Notes
(40 pages)
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Lecture 2A. Conditional Probability Introduction
34:00
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Lecture 2B. Conditional Probability Examples
26:00
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Lecture 2C. Introduction to Tree Diagram
32:00
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Annotated Notes Lecture 2 Conditional Probability
(70 pages)
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Lecture 3A. Total Probability
36:00
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Lecture: GATE PYQs Tree Method
95:00
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Annotated Notes Lecture GATE PYQs Tree Method
(35 pages)
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Lecture3B. Conditional Probability Examples
33:00
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Annotated Notes Lecture 3A-3B
(55 pages)
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Lecture 3C: GATE PYQs and Bayes Theorem
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Annotated Notes Lecture 3C: GATE PYQs and Bayes Theorem
(48 pages)
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[Optional] My LinkedIn Interview
49:00
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Annotated Notes [Optional] My Linkedin Interview
(28 pages)
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Lecture 4A. independence of events
43:00
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Lecture 4B. Conditional Independence
32:00
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Lecture 4C. Independence Does not imply Conditional Independence
22:00
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Annotated Notes Lecture 4C. Independence Does not imply Conditional Independence
(22 pages)
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Lecture 4d: Random Variables Introduction
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Annotated Notes Lecture 4d: Random Variables Introduction
(46 pages)
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Lecture 5A. Practice Questions on Random Variables
29:00
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Annotated Notes Lecture 5a. Practice Questions on Random Variables
(20 pages)
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Lecture 5B. Practice Questions on Random Variables
67:00
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Annotated Notes Lecture 5b. Practice Questions on Random Variables
(46 pages)
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Lecture 6A. Types of Random Variables
9:00
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Lecture 6B. Probability Mass Function Questions Part 1
20:00
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Lecture 6C. Probability Mass Function Questions Part 2
15:00
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Lecture 6 Annotated Notes
(39 pages)
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Lecture 7A. Expectation of Discrete Random Variable
19:00
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Lecture 7B Expectation vs Average
40:00
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Lecture 7C. Expectation as center of mass
8:00
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Lecture 7D. MIT Question and GATE 2021 Question
13:00
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Lecture 7E. Expectation of Random Variable which is a Function of Random Variable
18:00
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Lecture 7 Annotated Notes
(70 pages)
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Lecture 7F. Recursive Ways to find Expectation
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Annotated Notes Lecture 7F Recursive Ways to find Expectation
(35 pages)
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Lecture 8A. Question number 8
21:00
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Lecture 8B. Question number 9, 10, 11
17:00
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Lecture 8C. Question number 12, 13
30:00
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Lecture 8 Annotated Notes Expectation Tree Method
(37 pages)
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Lecture 9A. Cumulative Distribution Function
31:00
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Lecture 9B. Variance Intuition and Formula
52:00
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Lecture 9C. Variance Main Formula and Questions
38:00
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Lecture 9D. Variance Questions
45:00
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Annotated Notes Lecture 9 CDF and Variance
(85 pages)
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10A. Discrete random variable (Bernoulli and Binomial RVs)
77:00
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Lecture 10B. MIT Question on Binomial RV
16:00
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Lecture 10C. Optional and Skip - Question on Plot of PMF
19:00
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Lecture 10D. Poisson Random Variable
28:00
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Lecture 10E. Discrete Uniform Random Variable Introduction
15:00
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Annotated Notes Lecture 10 DRVs
(79 pages)
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Lecture 11A. Introduction to Continuous Distributions | Intuition about PDF
32:00
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Lecture 11B. Continuous Uniform Distribution
25:00
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Lecture 11C. Normal Distribution
43:00
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Lecture 11D. Normal Distribution - 2
17:00
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Lecture 11E. Exponential Distribution
18:00
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Annotated Notes Lecture 11 Continuous Random Variable
(83 pages)
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Lecture 12. Statistics - Mean Mode Median
35:00
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Annotated Notes Lecture 12 Mean Mode Median
(20 pages)
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Probability Module 2
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Lecture 1: Joint Probability Mass Function
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Annotated Notes Lecture 1 Module 2 Join PMFs
(78 pages)
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Lecture 2: Introduction to Conditional Expectation
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Annotated Notes Lecture 1 Module 2 Conditional Expectation
(59 pages)
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Lecture 3: Conditional Expectation, Total Expectation
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Annotated Notes Lecture 3 Module 2 Law of Total Expectation
(81 pages)
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Lecture 4: Continuous Random Variable, CDFs, and Expectation
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Annotated Notes Lecture 4 Module 2 Continuous Random Variable, CDFs, and Expectation
(111 pages)
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Lecture 5: Joint PDF, CDF, Conditional PDF, Conditional Expectation of Joint distribution
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Annotated Notes Lecture 5 Module 2 Joint PDF, Joint CDFs
(90 pages)
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Practice Set on Continuous Distributions
(51 pages)
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Lecture 6: (Solution of Practice Set) More Questions on Continuous RVs
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Annotated Notes Lecture 6 Module 2 Conditional Expectation in Continuous Case
(67 pages)
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Lecture 7: Covariance
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Annotated Notes Lecture 7 Module 2 Covariance
(103 pages)
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Lecture 8: Covariance Questions and Covariance Matrix
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Annotated Notes Lecture 8 Module 2 Covariance Questions and Covariance Matrix
(75 pages)
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Lecture 9: Solution of Weekly Quiz 10 on Conditional Expectation, Joint PMF, Joint CDF, Covariance, Covariance Matrix
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Lecture 10: Correlation and Many Questions on Correlation
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Annotated Notes Lecture 10 Module 2 Correlation
(60 pages)
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Hypothesis Testing
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Lecture 11 - Hypothesis Testing
92:00
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Annotated Notes Lecture 11 - Hypothesis Testing
(24 pages)
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Lecture 12 - Hypothesis Testing (Vishal Sir)
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Annotated Notes Lecture 12 - Hypothesis Testing-2
(46 pages)
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Lecture 13 - Hypothesis Testing (Vishal Sir)
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Annotated Notes Lecture 13 - Hypothesis Testing-3
(35 pages)
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