Basic Information

  • Course Code: EE325
  • Course Name: Probability and Random Processes
  • Course Offered In: Autumn 2022
  • Instructors: Prof. D Manjunath
  • Prerequisites: None
  • Difficulty (on a scale of 5): 3

Course Content

Sets and set operations; 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; Random sequences and modes of convergence (everywhere, almost everywhere, probability, distribution and mean square), Limit theorems, Strong and weak laws of large numbers, central limit theorem. Random process. Stationary processes. Mean and covariance functions. Ergodicity.

Feedback on Lectures

The lectures mostly covered the basic definitions of the terms and their simple applications. Each class began with a thorough recap of the previous few classes. The instructor spent a lot of time explaining each concept and clearing doubts. Little/No practice was taken, and the students were expected to practice on their own from the recommended textbook. No notes/slides were provided, and the students were expected to take notes on their own during the class, thus, I recommend attending all the lectures.

Feedback on Evaluations

The evaluation consisted of -

  1. Programming Assignments - 10%
  2. Quizzes - 15%
  3. Midsem - 25%
  4. Endsem - 50%

4 homework sheets were also given for practice(not graded), after which the quizzes were conducted. The exams mostly included easy and moderate-level questions and one or two hard ones.

Study Material and Resources

  1. A. Papoulis and S. Unnikrishna Pillai, `Probability, Random Variables and Stochastic Processes, McGraw Hill, Indian edition
  2. B Hajek

Follow-up Courses

A lot of options are available based upon your interests:

  • EE708: Information Theory
  • EE605: Error Correcting Codes
  • EE621: Markov Chains and Queueing Systems

Courses related to Communications, Signal Processing and Computer Science also utilise the concepts involved in probability theory.

Final Takeaway

This course is a foundation for many other courses in various domains such as Communications, Signal Processing and Computer Science. Try to attend all the lectures and practice as many questions as possible.