Convex Optimization and Approximation:
Optimization for Modern Data Analysis
EECS 227C/STAT 260
Spring 2016

Instructor: Ben Recht
Time:  TuTh 3:30-5:00 PM
Location: 3107 Etcheverry Hall

Office Hours: M 1-2. Tu 2:30-3:30.
Location: 572 Cory Hall

GSI: Rebecca Roelofs
Office Hours: F 8-10.
Location: 511 Soda Hall

Description: This course will explore theory and algorithms for nonlinear optimization. We will focus on problems that arise in machine learning and computational statistics, paying close attention to concerns about complexity, scaling, and implementation in these domains. Whenever possible, methods will be linked to particular application examples in data analysis. Topics will include

Required background: The prerequisites are previous coursework in linear algebra, multivariate calculus, probability and statistics. Some degree of mathematical maturity is also required. Coursework or background in optimization theory as covered in EE227BT is highly recommended. Numerical programming will be required for this course, so familiarity with MATLAB, R, numerical python, or an equivalent will be necessary.

Grading: There will be about four homeworks, which require some basic programming (50%). Students are required to scribe notes for one lecture (10%). There will be a take-home midterm and no final (20%). A course project will also be required (20%).

Homework: Homework assignments will be distributed on the bCourses site. Although it is acceptable for students to discuss the homework assignments with one another, each student must write up his/her homework on an individual basis. Each student must indicate with whom (if anyone) they discussed the homework problems. Homeworks must be turned in at the beginning of class on the due date. Hardcopies must be turned in. Do not submit the homework on bCourses. Late homeworks will not be accepted.

Scribing: All students are required to write up notes for one lecture. This will be graded the same as a homework assignment. Notes will be due one week after the scribed lecture. Because of the size of the class, two students will be selected per lecture. Partnering with a classmate is acceptable.

Midterm: The midterm will be handed out at 3:30PM on March 17 and due at 5PM on March 18. Students must work on this midterm alone.

Course project: The course project will involve independent work on a topic of the student's own choosing. Course projects will be presented in an informal poster session at the end of semester, and the work will be summarized in a write-up. The poster presentations will be during R&R week.


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