CS 761 - Randomzied Algorithms - Fall 2019
Randomness is a very powerful technique in algorithm design.
We will discuss tools and methods for harnessing the power of randomization in the design and analysis of algorithms and stochastic processes.
Prerequisites include basic knowledge of probability, and mathematical maturity.
- Lecture: Fridays, 10:00 AM - 12:50 PM, DC 2585
- Instructor: Gautam Kamath
- Office Hours: Fridays, 12:50 PM - 2:00 PM, DC 3124
Schedule subject to change.
Some topics which will hopefully be fit into the above schedule somewhere:
- Lecture 1 (September 6): Probability basics, randomized Quicksort, Markov's Inequality, Coupon Collectors (scribe notes, preliminary)
- Lecture 2 (September 13): Chebyshev's Inequality, Chernoff Bound, Minimum Cut, Minimum Spanning Tree
- Lecture 3 (September 20): TBD
- Lecture 4 (September 27): TBD
- Lecture 5 (October 4): TBD
- Lecture 6 (October 11): TBD
- Lecture 7 (October 25): TBD
- Lecture 8 (November 1): TBD
- Lecture 9 (November 8): TBD
- Lecture 10 (November 15): TBD
- Lecture 11 (November 22): TBD
- Lecture 12 (November 29): TBD
- Graph Algorithms
- Balls and bins, Hashing
- Streaming/Sketching/Dimension Reduction
- Probabilistic Methods
- Distribution Testing
- Linear Algebra
Books and References
No textbook is required for this course, though we will generally cover various topics from the book below by Motwani and Raghavan.
The following books are classic references in this area.
- "Randomized Algorithms," by Rajeev Motwani and Prabhakar Raghavan.
- "Probability and Computing: Randomized Algorithms and Probabilistic Analysis," by Michael Mitzenmacher and Eli Upfal.
- "The Probabilistic Method," by Noga Alon and Joel Spencer.
There will scribe notes (10%), 3 homework assignments (55%), and a final project (35%).
For scribe notes, please use the LaTeX template here.