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.
Texts: Recommended references: