COMP 480/580 Probabilistic Algorithms and Data Structures
- Instructor: Anshumali Shrivastava (anshumali AT rice)
- TA and Office Hours: TBA
- Class Timings: Tue/Thu 02:30PM - 03:45PM
- Location:KECK 100
- Instructor Office Hours: Tuesday/Thurdsay 3:45 to 4:15 after class, DH 2083
Recent Announcement
List of sample project topics link
Please sign up for lectures you want to scribe at excel
Overview
This course will be ideal for someone wanting to build a strong foundation in the theory and practice of algorithms for
processing Big-Data. We will discuss advanced data structures and algorithms going beyond deterministic setting and emphasize the role
of randomness in getting significant, often exponential, improvements in computations and memory.
Grading
Prerequisite
COMP 182 or equivalent required. COMP 382 is useful but not required. Basic Knowledge of
Probability. Knowledge of programming is required. Capability to manipulate primitive data structures such as arrays, list, etc. will
be needed for assignments.
Materials
Most of the materials needed will be posted on this website.
Some of the materials are fairly new and textbook is yet to be written.
A Nice Book to have is "Probability and Computing: Randomized Algorithms and Probabilistic Analysis
" by Michael Mitzenmacher and Eli Upfal.
Schedule
- 08/26 : CLASS CANCELLED
- 08/28 : Introduction, Logistics, and Timelines. slides
- 09/03 : Universal Hashing, Chaining, Linear Probing and Basic Analysis. slides
- 09/05 : Chaining Contd, Markov, Chebyshev's, and Chernoff Bounds. [slides1], [slides2], [scribes]
- 09/10 : Analysis of Hashing, Chaining and Probing. [slides] [scribes]
- 09/12 : Mark and Capture Experiments [slides] [scribes]
- 09/17 : Compressed Cache and Bloom Filters. (Why caching does not kill your memory) [slides]
- 09/19 : Resizing Hash Tables: Consistent Hashing and balanced allocations.(The idea behind Akamai Technologies) [slides]
- 09/24 : Special Topic SPOCA: A Stateless, Proportional, Optimally-Consistent Addressing Algorithm. (Best paper in USENIX 2011)
- 09/26 : Introduction to Stream Computing and Reservoir Sampling. (Algorithms at the Edges)
- 09/01 : Stream Estimation 1: Count-Min Sketch (Generalized Bloom Filters)
- 10/03 : Stream Estimation 2: Count-Sketches, Count Distinct and Norm Estimation on Streams.
- 10/08 : Minwise Hashing, Near-Duplicate Detection and LSH 1. (What is "hash function" in this NY-Times article)
- 10/10 : Minwise Hashing, Near-Duplicate Detection and LSH 2.
- 10/15 : MIDTERM RECESS
- 10/17 : Basic Sampling. (Beyond Random Sampling)
- 10/22 : Basic Sampling 2
- 10/24 : More LSH (Eliminate Pairwise Comparisons)
- 10/29 : Advanced Sampling. LSH as Computationally Efficient Importance Samplers. (Adaptive Sampling at the Cost of Random Sampling)
- 10/31 : DNF Counting(A closer Look at Estimation via Sampling).
- 10/05 : Election Day
- 11/07 : In Class Mid-Term Exam.
- 11/12 : Markov Chains, Stationary distributions, MCMC 1 (Sampling in Complex Spaces).
- 11/14 : Markov Chains, Stationary distributions, MCMC 2.
- 11/19 : Markov Chains, Stationary distributions, MCMC 3.
- 11/21 : Markov Chains, Stationary distributions, MCMC 4.
- 11/26 : Randomized Routing
- 11/28 : THANKSGIVING RECESS
- 11/03 : Estimation on Graphs
- 11/05 : Special Topics
Students with Disability
If you have a documented disability that may affect academic performance, you should: 1) make sure this documentation is on file with Disability Support Services (Allen Center, Room 111 / adarice@rice.edu / x5841) to determine the accommodations you need; and 2) meet with me to discuss your accommodation needs.