Semester: Autumn 2023 and Autumn 2024
Instructor: Prof. Sujay Kadam
This undergraduate course builds core probabilistic intuition through:
Discrete and continuous probability models
Conditional probability, Bayes theorem
Random variables, expectation, variance
Common distributions (Binomial, Poisson, Gaussian)
Multiple random variables and joint distributions
Central Limit Theorem and law of large numbers
Random processes and autocorrelation
Queuing Theory
Approach: Applications in communications, signal processing, and statistical reasoning with real-world modeling problems.