Advanced Algorithms
CS/CMS 139
Spring 2015

Tuesday/Thursday 1 - 2:25pm
Annenberg 105

Recitation Fridays 11-12pm or 1-2pm
Annenberg 314

Instructors

Katrina Ligett, 316 Annenberg. Office hours Monday 3-4pm, Annenberg 316.

Thomas Vidick, 207 Annenberg. Office hours Monday 2-3pm, Annenberg 207.

Teaching Assistants

Ramya Korlakai Vinayak. Office hours Tuesday 3-4pm.

Rachel Cummings. Office hours Wednesday 4-5pm, Annenberg 327.

Jenish Mehta. Office hours Wednesday 10-11am

We'll be using piazza for all class-related discussions. Please direct your questions and comments to the course website on piazza.

Description

This is an advanced algorithms course specially designed for the new CMS Ph.D. program. The course covers advanced topics in the design and analysis of algorithms, emphasizing modern techniques as well as applications in areas of current research interest. Topics covered will include multiplicative weight updates and experts, online learning, semidefinite programming and approximation algorithms, randomized algorithms, concentration bounds and derandomization, spectral methods and analysis of random walks, property testing.

Prerequisites

Ma 2, Ma 3, Ma/CS 6a, CS 21, CS 38/138, ACM/EE/CMS 116, or instructor’s permission.
This should not be your first course in algorithms. If you have not previously taken undergraduate-level algorithms, please register for CS38 or CS138 instead.

Evaluation

Your grade in the course will be based on a mix of participation, homeworks, course notes scribing, and midterm/final exams. This is a preliminary breakdown that may change during the term, particularly as enrollment levels settle:

Attendance/participation 5%
Scribing of notes for one lecture 10%
Homework 25%
Midterm 25%
Final 35%

Resources

We won't be following any particular textbooks. Standard books you may find useful include Randomized Algorithms by Motwani and Raghavan, Approximation Algorithms by Vazirani, The Probabilistic Method by Alon and Spencer.
Links to similar courses taught at other universities which have lecture notes online:

Linear and Semidefinite Programming, Advanced Approximation Algorithms and A Theorist's Toolkit, taught by Ryan O'Donnell at CMU.
Combinatorial Algorithms and Data Structures, taught by Satish Rao at Berkeley.
Mathematical Toolkit, taught by Madhur Tulsiani at TTI/University of Chicago.
Advanced Algorithm Design, taught by Sanjeev Arora at Princeton.

See also the following notes available online:

Semidefinite Programs and Combinatorial Optimization, by Laslo Lovasz.