## IT211

### Course Name:

Probability and Statistics for AI (IT211)

### Programme:

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### Credits (L-T-P):

### Content:

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