Instructor: Greg Valiant
CAs: Balaji Balachndran
Course Description: Randomness pervades the natural processes around us, from the formation of networks, to genetic recombination, to quantum physics. Randomness is also a powerful tool that can be leveraged to create algorithms and data structures which, in many cases, are more efficient and simpler than their deterministic counterparts. This course covers the key tools of probabilistic analysis, and application of these tools to understand the behaviors of random processes and algorithms. Emphasis is on theoretical foundations, though we will apply this theory broadly, discussing applications in machine learning and data analysis, networking, and systems. Topics include tail bounds, the probabilistic method, Markov chains, and martingales, with applications to analyzing random graphs, metric embeddings, random walks, and a host of powerful and elegant randomized algorithms.
Prerequisites: Prerequisites: CS 161 and STAT 117/118, or equivalents and instructor consent.
When/Where: Class is T/TH, 10:30am-11:50pm in CoDa B60.
Office hours.
NOTE: Office hours start in Week 2. There are no OH in Week 1, but please ask on Ed if you have a question.
Deviations from this schedule will be announced on Canvas.
CS265/CME309 is a "flipped class." This means that you will watch short recorded mini-lectures and/or read lecture notes before class, and come to class ready to engage. In class, we will do active learning to practice and further develop the material from the mini-lectures. The agendas for each class (exercises and solutions) will be posted on this website (in the class-by-class resources below).
Since a large part of learning will happen during active in-class group work, we encourage you to attend class if possible.
Exception: Please do not come to class if you are sick.
The homework schedule is listed below. The topics are estimates; in particular, previous material is always fair game :)
Solutions will be posted on Canvas.
We will have two in-person, proctored exams:
Your grade is made up of:
There will be several options throughout the quarter for you to go above and beyond. (For example, bonus "might be fun to think about" problems, links to further reading, etc). These things do not factor directly into your grade, but they will factor into your learning! The course staff will be happy to give you feedback on any of these sorts of things, but we won't officially grade them.
Below, find class-by-class resources, including lecture notes, in-class agendas and exercises, and further reading. All videos can be found Canvas, or also on the YouTube playlist here.
Classes that have not happened yet may have broken links or links to draft (last year's) materials, and are subject to change.