### EE 325 – PROBABILITY AND RANDOM PROCESSES

**Course offered in:**

Autumn 2016

**Professor**

Prof. Animesh Kumar

**Course Content**

**Motivation –**

Probability and random processes is integral to signal processing, communications and machine learning. It finds wide application across diverse fields beyond elec, including economics, finance etc.

**Course content –**

Probability space, Conditional probability and Bayes theorem,Combinatorial probability and sampling models, Discrete random variables, probability mass function, probability distribution function, example random variables and distributions, Continuous random variables, probability density function, probability distribution function, example distributions, Joint distributions, functions of one and two random variables, moments of random variables, Conditional distribution, densities and moments, Characteristic functions of a random variable, Markov, Chebyshev and Chernoff bounds; Strong and weak laws of large numbers, central limit theorem.Random process. Stationary processes.

**Lectures –**

The lectures were very well planned and sir explained the concepts with a few solved examples in class as well. His taught on the blackboard, and did not follow any text as such, but covered all the topics in class, writing extensively on the blackboard, so note-taking was an easy affair in his classes.

He gave homework problems once every two weeks, to be submitted a week later. The problems were moderate, with a few difficult ones. The solutions to the homework problems were uploaded before the exams.

The Prof took 2 mid-semester exams, one end-sem and no quizzes.

**Feedback on exams –**

The examinations were on the difficult side. A lot questions involved heavy mathematical manipulations, and time was often a constraint.

**Reference –**

The lecture notes were extensive, for the theory.

**For further reading –**

Probability, Random processes and stochastic processes by Papoulis

**Reviewed by –**

Dhruti Shah (dhruti96shah@gmail.com)