CS 294-202 Pseudorandomness

Course Description:

Randomized algorithms give a broad and rich algorithmic toolkit (e.g., sampling, Monte Carlo methods). Randomness is an essential resource in distributed computing, cryptography, and interactive proofs. In this course, we would explore the role of randomness in computation: Can we derandomize algorithms without changing their time or space complexity? Can we "purify" randomness from a weak source of randomness? 

Pseudo-randomness is the property of "appearing random" while having little or no randomness. Pseudo-randomness plays a significant role in error-correcting codes, expander graphs, randomness extractors, and pseudo-random generators. In this course, we will see all these beautiful applications. In the second part of the course, we would focus on the question of derandomization of small-space computation, also known as the "RL versus L" question. It asks whether all problems that can be decided in randomized logarithmic space (RL) can also be decided in deterministic logarithmic space (L). We would cover recent approaches towards showing that RL = L. 

Undergraduate students who wish to take this class should fill out the following Google Form.

General Information: 

Time and Place: Tuesday, Thursday 3:30-5:00 PM, Soda 405

Instructor: Avishay Tal, atal "at" berkeley.edu
TA: TBD

Grading: TBD

Prereqs: CS170 or equivalent is required.

Textbook:
Problem Sets:

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