Probability and Stochastic Processes
This course provides a mathematical introduction to probability and stochastic processes. While the main focus is discrete probability and combinatorial analysis, some continuous probability is discussed. Examples and applications are emphasized over theory.
The main topics in approximate order are:
Conditional Expectation
Simple Random Walk
Markov Chains
Martingales in discrete time
Poisson and some other jump process
A first introduction to Brownian motion
The mathematical prerequisite for the course is an undergraduate (post-calculus) course in probability and/or statistics.
This course takes place in the month of September, prior to the start of Autumn Quarter for the in-person cohort.
In-Person Quarter: September Launch
In-Person Instructor: Greg Lawler
In-Person Syllabus
Online Quarter: Winter 2026
Online Instructor: Sayan Das
Online Syllabus