E0 315: Measure Theoretic Probability, January Term 2022

E0 315: Measure Theoretic Probability
  (Jan-Apr 2022)

Department of Computer Science & Automation
Indian Institute of Science

[Course Description]  [Academic Honesty]  [Syllabus] 

Course Information

Class Meetings

Lectures: Tuesday, Thursday 3:30pm-05:00pm
MS Teams (online)
First lecture: Thu, Jan 6th


Prof. Ambedkar Dukkipati (ambedkar@iisc)
Prof. Shalabh Bhatnagar (shalabh@iisc)

Course Evaluation

Mid Term: 30 Marks
Assignments: 20 Marks
Endsem: 50 Marks


Course Description

The objective of the course is to provide students with a measure-theoretic understanding of probability that is normally not taught in Engineering departments. The hope is this would equip students with deeper concepts and tools in probability theory that would help them in understanding deeper concepts in Machine Learning, Reinforcement Learning, Analysis of Stochastic Approximation Algorithms etc.

Preferred background

Basic Probability and Analysis

Academic Honesty

As students of IISc, we expect you to adhere to the highest standards of academic honesty and integrity.

Elements of the course are designed to support your learning of the subject. Copying will not help you (in the exams or in the real world), so don't do it. If you have difficulties learning some of the topics or lack some background, try to form study groups where you can bounce off ideas with one another and try to teach each other what you understand. You're also welcome to talk to any of us and we'll be glad to help you.

If any exam/assignment is found to be copied, it will automatically result in a zero grade for that exam/assignment and a warning note to your advisor. Any repeat instance will automatically lead to a failing grade in the course.


Sigma-Field, Construction of Probability Spaces and Measures, Random Variables and Measurability, Independence, Integration and Expectation, Monotone Convergence, Dominated Convergence, almost sure, and in-probability convergence, Convergence in Distribution, Central Limit Theorem, Conditional Expectation and Martingales.