NOC:Probability Foundations for Electrical Engineers (USB)

Media Storage Type : 64 GB USB Stick (2 Nos.)

NPTEL Subject Matter Expert : Prof. R.Aravind, Dr. Andrew Thangaraj

NPTEL Co-ordinating Institute : IIT Madras

NPTEL Lecture Count : 73

NPTEL Course Size : 74 GB

NPTEL PDF Text Transcription : Available and Included

NPTEL Subtitle Transcription : Available and Included (SRT)

Lecture Titles:

Lecture 1 - Experiments, Outcomes and Events
Lecture 2 - Examples: Experiments and sample spaces
Lecture 3 - Operations on Events
Lecture 4 - Examples: Sample spaces and events
Lecture 5 - Sigma Fields and Probability
Lecture 6 - Discrete Sample Spaces
Lecture 7 - Union and Partition
Lecture 8 - Examples: Probability Calculation for Equally likely Outcomes
Lecture 9 - Definition and Basic Properties
Lecture 10 - Bayes' Rule for Partitions
Lecture 11 - Examples: Conditional probability
Lecture 12 - Example of Detection
Lecture 13 - Example: Coloured Cards from a Box
Lecture 14 - Independence of Events
Lecture 15 - Examples: Independence
Lecture 16 - Combining Independent Experiments
Lecture 17 - Conditional Independence
Lecture 18 - Examples and Computations with Conditional Independence
Lecture 19 - Binomial and Geometric Models
Lecture 20 - Examples: Binomial and Geometric Model
Lecture 21 - Definition and Discrete Setting
Lecture 22 - RandomVariables and Events
Lecture 23 - Examples: Discrete random variables
Lecture 24 - Important distributions
Lecture 25 - Examples: Discrete PMFs
Lecture 26 - Real-life modeling example
Lecture 27 - More Distributions
Lecture 28 - Conditional PMFs, Conditioning on an event, Indicator random variables
Lecture 29 - Example: Conditioning on an event, Indicator random variables
Lecture 30 - Multiple random variables and joint distribution
Lecture 31 - Example: Two random variables
Lecture 32 - Marginal PMF
Lecture 33 - Trinomial joint PMF
Lecture 34 - Events and Conditioning with Two Random Variables
Lecture 35 - Example: compute marginal and conditional PMFs, probability of events
Lecture 36 - Independent random variables
Lecture 37 - More on independence
Lecture 38 - Example: IID Repetitions
Lecture 39 - Addition of Random Variables
Lecture 40 - Sum, Difference and Max of Two Random Variables
Lecture 41 - More Computations: Min of Two Random Variables
Lecture 42 - Example: X+Y, X-Y, min(X,Y), max(X,Y)
Lecture 43 - Real line as sample space
Lecture 44 - Probability density function (pdf)
Lecture 45 - Cumulative distribution function (CDF)
Lecture 46 - Continuous random variables
Lecture 47 - pdf and CDF of continuous random variables
Lecture 48 - Spinning pointer example
Lecture 49 - Important continuous distributions
Lecture 50 - More continuous distributions
Lecture 51 - Two-dimensional real sample space
Lecture 52 - Joint pdf and joint CDF
Lecture 53 - More on assigning probability to regions of x-y plain
Lecture 54 - Darts example and marginal pdfs
Lecture 55 - Independence to two continuous random variables
Lecture 56 - Examples: two independent continuous random variables
Lecture 57 - Prob[ X > Y ]: computation of probability of a non-rectangular region
Lecture 58 - Transformations of random variables
Lecture 59 - CDF method
Lecture 60 - pdf method
Lecture 61 - Examples
Lecture 62 - One-to-one transformations
Lecture 63 - Expected Value or Mean of a Random Variable
Lecture 64 - Properties of Expectation
Lecture 65 - Expectation Computations for Important Distributions
Lecture 66 - Variance
Lecture 67 - Examples of Variance
Lecture 68 - Expectations with Two Random Variables
Lecture 69 - Correlation and Covariance
Lecture 70 - Examples: Continuous Distributions
Lecture 71 - Examples: Symmetry
Lecture 72 - Examples: Discrete Distributions
Lecture 73 - Live Session