Modules / Lectures
Module NameDownload
noc20_mg01_assignment_Week_0noc20_mg01_assignment_Week_0
noc20_mg01_assignment_Week_1noc20_mg01_assignment_Week_1
noc20_mg01_assignment_Week_10noc20_mg01_assignment_Week_10
noc20_mg01_assignment_Week_11noc20_mg01_assignment_Week_11
noc20_mg01_assignment_Week_12noc20_mg01_assignment_Week_12
noc20_mg01_assignment_Week_2noc20_mg01_assignment_Week_2
noc20_mg01_assignment_Week_3noc20_mg01_assignment_Week_3
noc20_mg01_assignment_Week_4noc20_mg01_assignment_Week_4
noc20_mg01_assignment_Week_5noc20_mg01_assignment_Week_5
noc20_mg01_assignment_Week_6noc20_mg01_assignment_Week_6
noc20_mg01_assignment_Week_7noc20_mg01_assignment_Week_7
noc20_mg01_assignment_Week_8noc20_mg01_assignment_Week_8
noc20_mg01_assignment_Week_9noc20_mg01_assignment_Week_9


Sl.No Chapter Name MP4 Download
1Lecture 1: Sample Space and eventsDownload
2Lecture 2: Axioms of ProbabilityDownload
3Lecture 3: Independence of events and Conditional ProbabilityDownload
4Lecture 4: Baye’s Theorem and Introduction to Random VariablesDownload
5Lecture 5: CDF and it’s propertiesDownload
6Lecture 6: Continuity of ProbabilityDownload
7Lecture 7: Discrete and Continuous random variablesDownload
8Lecture 8: Expectation of random variables and its propertiesDownload
9Lecture 9: Variance and some inequalities of random variablesDownload
10Lecture 10: Discrete Probability DistributionsDownload
11Lecture 11: Continuous Probability DistributionsDownload
12Lecture 12: Jointly distributed random variables and conditional distributionsDownload
13Lecture 13: Correlation and CovarianceDownload
14Lecture 14: Transformation of random vectorsDownload
15Lecture 15: Gaussian random vector and joint Gaussian distributionDownload
16Lecture 16: Random ProcessesDownload
17Lecture 17: Properties of random ProcessDownload
18Lecture 18: Poisson ProcessDownload
19Lecture 19: Properties of Poisson Process (Part 1)Download
20Lecture 20: Properties of Poisson Process (Part 2)Download
21Lecture 21: Convergence of sequence of random variables (Part 1)Download
22Lecture 22: Convergence of sequence of random variables (Part 2)Download
23Lecture 23: Relation between different notions of convergenceDownload
24Lecture 24: Cauchy’s criteria of convergenceDownload
25Lecture 25: Convergence in expectationDownload
26Lecture 26: Law of Large NumbersDownload
27Lecture 27: Central limit theoremDownload
28Lecture 28: chernoff boundDownload
29Lecture 29: Introduction to Markov propertyDownload
30Lecture 30: Transition Probability MatrixDownload
31Lecture 31: Finite dimensional distribution of Markov chainsDownload
32Lecture 32: Strong Markov PropertyDownload
33Lecture 33: Stopping TimeDownload
34Lecture 34: Hitting Times and RecurrenceDownload
35Lecture 35: Mean Number of returns to a stateDownload
36Lecture 36: Communicating classes and class propertiesDownload
37Lecture 37: Class Properties ContinuedDownload
38Lecture 38: Positive Recurrence and The Invariant Probability VectorDownload
39Lecture 39: Properties of Invariant Probability VectorDownload
40Lecture 40: Condition For TransienceDownload
41Lecture 41: Example of QueueDownload
42Lecture 42: Queue Continued and Example of Page RankDownload
43Lecture 43: Introduction to renewal TheoryDownload
44Lecture 44: The Elementary Renewal TheoremDownload
45Lecture 45: Application to DTMCDownload
46Lecture 46: Renewal Reward TheoremDownload
47Lecture 47: Introduction to Continuous Time Markov ChainsDownload
48Lecture 48: Properties of states in CTMCDownload
49Lecture 49: Embedded markov chainDownload

Sl.No Chapter Name English
1Lecture 1: Sample Space and eventsDownload
Verified
2Lecture 2: Axioms of ProbabilityDownload
Verified
3Lecture 3: Independence of events and Conditional ProbabilityDownload
Verified
4Lecture 4: Baye’s Theorem and Introduction to Random VariablesDownload
Verified
5Lecture 5: CDF and it’s propertiesDownload
Verified
6Lecture 6: Continuity of ProbabilityDownload
Verified
7Lecture 7: Discrete and Continuous random variablesDownload
Verified
8Lecture 8: Expectation of random variables and its propertiesDownload
Verified
9Lecture 9: Variance and some inequalities of random variablesDownload
Verified
10Lecture 10: Discrete Probability DistributionsDownload
Verified
11Lecture 11: Continuous Probability DistributionsDownload
Verified
12Lecture 12: Jointly distributed random variables and conditional distributionsDownload
Verified
13Lecture 13: Correlation and CovarianceDownload
Verified
14Lecture 14: Transformation of random vectorsDownload
Verified
15Lecture 15: Gaussian random vector and joint Gaussian distributionDownload
Verified
16Lecture 16: Random ProcessesDownload
Verified
17Lecture 17: Properties of random ProcessDownload
Verified
18Lecture 18: Poisson ProcessDownload
Verified
19Lecture 19: Properties of Poisson Process (Part 1)Download
Verified
20Lecture 20: Properties of Poisson Process (Part 2)Download
Verified
21Lecture 21: Convergence of sequence of random variables (Part 1)Download
Verified
22Lecture 22: Convergence of sequence of random variables (Part 2)Download
Verified
23Lecture 23: Relation between different notions of convergenceDownload
Verified
24Lecture 24: Cauchy’s criteria of convergenceDownload
Verified
25Lecture 25: Convergence in expectationDownload
Verified
26Lecture 26: Law of Large NumbersDownload
Verified
27Lecture 27: Central limit theoremDownload
Verified
28Lecture 28: chernoff boundDownload
Verified
29Lecture 29: Introduction to Markov propertyDownload
Verified
30Lecture 30: Transition Probability MatrixDownload
Verified
31Lecture 31: Finite dimensional distribution of Markov chainsDownload
Verified
32Lecture 32: Strong Markov PropertyDownload
Verified
33Lecture 33: Stopping TimeDownload
Verified
34Lecture 34: Hitting Times and RecurrenceDownload
Verified
35Lecture 35: Mean Number of returns to a stateDownload
Verified
36Lecture 36: Communicating classes and class propertiesDownload
Verified
37Lecture 37: Class Properties ContinuedDownload
Verified
38Lecture 38: Positive Recurrence and The Invariant Probability VectorDownload
Verified
39Lecture 39: Properties of Invariant Probability VectorDownload
Verified
40Lecture 40: Condition For TransienceDownload
Verified
41Lecture 41: Example of QueueDownload
Verified
42Lecture 42: Queue Continued and Example of Page RankDownload
Verified
43Lecture 43: Introduction to renewal TheoryDownload
Verified
44Lecture 44: The Elementary Renewal TheoremDownload
Verified
45Lecture 45: Application to DTMCDownload
Verified
46Lecture 46: Renewal Reward TheoremDownload
Verified
47Lecture 47: Introduction to Continuous Time Markov ChainsDownload
Verified
48Lecture 48: Properties of states in CTMCDownload
Verified
49Lecture 49: Embedded markov chainDownload
Verified


Sl.No Language Book link
1EnglishNot Available
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available