Welcome to the Spring 2020 homepage for CMPSCI 690RA.
- Instructor:
- Andrew McGregor. (mcgregor at cs.umass.edu)
- Office hours: Tuesday, 11:30am to 12:30 pm in CMPS 334.
- Lectures:
- Tuesday and Thursday, 10am to 11:15 pm in CMPS 140.
- Course Description:
- An introduction to some more advanced algorithmic topics with a focus on randomization and probabilistic techniques. Topics will include the probabilistic method; tail inequalities; entropy and information; random walks; derandomization and limited independence. Applications to online algorithms; approximation and combinatorial optimization; data-stream computation; and communication theory will be discussed as time permits. Prerequisite is CMPSCI 514 or 611 or equivalent.
- Textbook:
We will use material from the following books:
- Randomized Algorithms, Motwani and Raghavan (Required)
- Probability and Computing, Mitzenmacher and Upfal (Useful)
- Lecture Slides:
- Lecture 1: Intro
- Lecture 2: Probability Basics and Tail Bounds
- Lecture 3: Balls and Bins
- Lecture 4: Stable Matchings and Deferred Decision
- Lecture 5 and 6: Wiring and Maximum Satisfiability
- Lecture 7: Markov Chains and Random Walks
- Lecture 8: More Markov Chains and Coupling
- Lecture 9: Monte Carlo Algorithms
- Lecture 10: MCMC Algorithms
- Lecture 11, 12, 13: Probabilistic Method and Lovasz Local Lemma
- Lecture 14-15: Fingerprints and Communication Complexity
- Lecture 16-18: Sublinear Time Graph Algorithms
- Lecture 19-20: Graph Property Testing
- Lecture 21-22: Graph Sketching
- Lecture 23-24: Graph Sparsification and Sketching
- Lecture 25: Data Streams
- Homeworks and Exams: