CS5330: Randomized Algorithms, Spring 2020.
Instructors: Arnab Bhattacharyya
Teaching Assistant: Bao Ergute.
This publicfacing webpage does not contain the lecture notes and problems sets that were distributed in the class.
Course Description
Randomization is ubiquitous in computer science now. In this module, we cover topics in the following broad areas:
 Basic probability
 Concentration of measure
 Hashing
 Randomized data structures
 Randomized approximation algorithms
 Random walks on graphs
 Approximate counting and sampling
 Online algorithms
Teaching Modes
 Lecture from 6:30 pm to 8:30 pm at I30344. There will be 23 breaks, so the actual lecture time will be about 90 minutes.
 Tutorial from 8:30 pm to 9:30 pm at I30344. We will discuss the takehome problems assigned from two weeks ago, as well as other burning questions.
 Consultation hour with TA (Bao Ergute) at Database Lab 1 on Fridays, 6:30 pm to 8:30 pm.
 Consultation by appointment with Prof. Arnab. I am also available after lecture and tutorial.
References
 Probability and Computing. Mitzenmacher and Upfal. 2nd Edition, 2017. Highly recommended that you get it.
 Additional reference: Randomized Algorithms. Motwani and Raghavan. 1995. A good reference but not really necessary.

Many lecture notes scattered around the web.