Course Name: 

Probability and Statistics for AI (AI203)


B.Tech (AI)


Programme Core (PC)

Credits (L-T-P): 

(3-0-2) 4


Definition of Probability; Counting Principle for equally likely outcomes; probability rules; independence; system reliability (parallel, series); Conditional Probability, Law of Total Probability, Bayes Rule; Definition of Random Variable, Discrete Random Variables Bernoulli, Binomial; probability mass function; Binomial, Hyper geometric, Geometric, Negative Binomial, Poisson and Poisson approximation of Binomial; Expectation and Variance of a Discrete Random Variable; Continuous Distributions (density), including joint distributions and joint density mean and variance of a density; Gaussian density; Exponential and Gamma densities, Central Limit Theorem; Simulation of Random Variables, Statistics and sampling distribution of the sample mean; Statistics and sampling distribution of the sample proportion; Statistical inference; Parameter Estimation (Method of Moments, Maximum Likelihood Method); Confidence Intervals (Pivotal Quantity Method) Hypothesis Testing; type I and type II errors; anomalous events and how to identify them


DeGroot & Schervish, Probability and Statistics (4th Edition) Pearson (2011).
Wasserman, All of Statistics: A Concise Course in Statistical Inference Springer (2004).


Information Technology

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Department of Information Technology,
National Institute of Technology Karnataka,
SurathkalP. O. Srinivasnagar, Mangalore - 575 025
Ph.:    +91-824-2474056
Email:  hodit [at] nitk [dot] edu [dot] in

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