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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

Term Project or Final Exam: 30%
Assignments: 40%
Mid-Term Exam: 20%
Lecture Scribing: 10%

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 (scribes and slides) 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

08/23 : Introduction, Logistics, Mark and Re-capture Estimation. [Scribe] [Slide]
08/25 : Brief Pseudo Randomness, Universal Hashing, Chaining, and Linear Probing. [Slide]
08/30 : Markov, Chebyshev's, and Chernoff Bounds. [Scribe][Slide]
09/01 : Analysis of Hashing, Chaining and Probing.[Scribe]
09/06 : Compressed Cache and Bloom Filters. (Why caching does not kill your memory)[Scribe]
09/08 : Resizing Hash Tables: Consistent Hashing and balanced allocations.(The idea behind Akamai Technologies)[Scribe]
09/13 : Special Topic SPOCA: A Stateless, Proportional, Optimally-Consistent Addressing Algorithm. (Best paper in USENIX 2011)[Scribe]
09/15 : Introduction to Stream Computing and Reservoir Sampling. (Algorithms at the Edges)[Scribe]
09/20 : Stream Estimation 1: Count-Min Sketch (Generalized Bloom Filters)[Scribe]
09/22 : Stream Estimation 2: Count-Sketches [Scribe]
09/27 : Stream Estimation 3: Count Distinct and Norm Estimation on Streams.[Scribe]
09/29 : Minwise Hashing, Near-Duplicate Detection and LSH 1. (What is "hash function" in this NY-Times article)[Scribe]
10/04 : Minwise Hashing, Near-Duplicate Detection and LSH 2. [Scribe]
10/06 : Reserve a SLOT for Guest Lecture
10/11 : MIDTERM RECESS
10/13 : Basic Sampling. (Beyond Random Sampling)[Scribe]
10:18 : Basic Sampling 2 [Scribe]
10/20 : More LSH (Eliminate Pairwise Comparisons)
10/25 : Advanced Sampling. LSH as Computationally Efficient Importance Samplers. (Adaptive Sampling at the Cost of Random Sampling)
10/27 : DNF Counting(A closer Look at Estimation via Sampling).
11/01 : In Class Mid-Term Exam.
11/03 : Markov Chains, Stationary distributions, MCMC 1 (Sampling in Complex Spaces).
11/08 : Markov Chains, Stationary distributions, MCMC 2.
11/10 : Markov Chains, Stationary distributions, MCMC 3.
11/15 : Markov Chains, Stationary distributions, MCMC 4.
11/17 : Randomized Routing
11/22 : Estimation on Graphs
11/24 : THANKSGIVING RECESS
11/29 : Special Topics
12/01 : 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.