Logistics: 
Tue/Thu 2:00  3:30
GDC 4.304
Unique Number: 53295
Course web page:
http://www.cs.utexas.edu/~ecprice/courses/sublinear/

Professor: 
Eric Price
Email: ecprice@cs.utexas.edu
Office: GDC 4.510
Office Hours: Tuesday 3:305, Wednesday 34

Useful References: 
Similar courses
include Sublinear
Algorithms (at
MIT), Algorithms
for Big Data (at Harvard),
and Sublinear
Algorithms for Big Datasets (at the University of Buenos
Aires).

Content: 
This graduate course will study algorithms that can process
very large data sets. In particular, we will consider
algorithms for:
 Data streams, where you don't have enough space to
store all the data being generated.
 Property testing, where you don't have enough time to
look at all the data.
 Compressed sensing, where you don't have enough
measurement capacity to observe all the data.

Problem Sets: 
 Problem Set 1. Due September 23.
 Problem Set 2. Due October 9.
 Problem Set 3. Due October 28.
 Problem Set 4. Due November 11.
 Problem Set 5. Due November 25.

Lectures: 
 Thursday, August 28. Course overview; uniformity testing;
beginning of distinct elements. [Lecture notes
(pdf) (tex)]
 Tuesday, September 2. Concentration of measure; Markov, Chebyshev, subgaussians, subexponentials, JohnsonLindenstrauss. [Lecture notes (pdf) (tex)]
 Thursday, September 4. Continue distinct elements; streaming turnstile model; AMSsketch. [Lecture notes (pdf) (tex)]
 Tuesday, September 9. CountMin, CountSketch, Fourier analysis of CountSketch. [Lecture notes (pdf) (tex)]
 Thursday, September 11. More CountSketch; sparse recovery with sublinear time. [Lecture notes (pdf) (tex)]
 Tuesday, September 16. L0 sampling. Graph sketching preliminaries. [Lecture notes (pdf) (tex)]
 Thursday, September 18. Graph sketching.
 Tuesday, September 23. Coresets. [Lecture notes (pdf) (tex)]
 Thursday, September 25. Fp estimation.
 Tuesday, September 30. Covering and packing numbers, RIP.[Lecture notes (pdf) (tex)]
 Thursday, October 2. Compressed Sensing, Iterative Hard Thresholding.[Lecture notes (pdf) (tex)]
 Tuesday, October 7. ModelBased Compressed Sensing, L1 minimization.[Lecture notes (pdf) (tex)]
 Thursday, October 9. l2/l1 upper bound. deterministic l2/l2 lower bound. randomized l1/l1 lower bound.[Lecture notes (pdf) (tex)]
 Tuesday, October 14. Project ideas. RIP1. SSMP.[Lecture notes (pdf) (tex)]
 Thursday, October 16. Adaptivity, group testing, entropy.[Lecture notes (pdf) (tex)]
 Tuesday, October 21. Communication complexity and information cost.[Lecture notes (pdf) (tex)]
 Thursday, October 23. Adaptive sparse recovery, introduction to Fourier transform.[Lecture notes (pdf) (tex)]
 Tuesday, October 28. Getting familiar with Fourier transforms.
 Thursday, October 30. Nonequispaced Fourier transforms.
 Tuesday, November 4. Sparse Fourier transforms.
 Thursday, November 6. RIP of subsampled Fourier.[slides]
 Tuesday, November 11. Oblivious Subspace Embeddings.[Lecture notes (pdf) (tex)]
The tentative outline for the rest of the course is as follows:
 Sparse JL
 Bloom filters; invertible bloom lookup tables
 Property testing: uniformity testing
 More property testing
 Other streaming models: random order, distributional

Prerequisites: 
Mathematical maturity and comfort with undergraduate algorithms and
basic probability. Ideally also familiarity with linear algebra.

Grading: 
40%: Homework
30%: Final project
20%: Scribing lectures
10%: Participation

Scribing: 
In each class, one student will be assigned to take notes.
These notes should be written up in
a standard LaTeX format before
the next class.

Homework policy: 
There will be a homework assignment roughly every two
weeks.
Collaboration policy: You are encouraged to
collaborate on homework. However, you must write up your own
solutions. You should also state the names of those you
collaborated with on the first page of your submission.

Final project: 
In lieu of a final exam, students will perform final
projects. These may be done individually or in groups of
23. An ideal final project would perform a piece of
original research in a topic related to the course. Failing
that, one may perform a literature survey covering several
research papers in the field.
Students will present their results to the class during
the last week of classes. The final paper will be due on
the scheduled final exam day.

Students with
Disabilites:

Any student with a documented disability (physical or
cognitive) who requires academic accommodations should contact the
Services for Students with Disabilities area of the Office of the
Dean of Students at 4716259 (voice) or 4714641 (TTY for users
who are deaf or hard of hearing) as soon as possible to request an
official letter outlining authorized accommodations.
