Optimization Models and Applications
Instructors: A. Bayen
This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems.
Students will gain familiarity with the mathematical machinery underlying convex optimization, including matrix-vector calculus and fundamental concepts of linear algebra.
The image below shows a graph of the Senators in the 2004-2006 US Senate, that is obtained by solving a specific optimization problem involving the estimation of covariance matrices with sparsity constraints. (For more details, see here.)
We do not use this site to communicate, post homeworks, etc. We use bCourses and Piazza for course discussions and announcements.
Link to UC Berkeley Schedule of classes: EECS 127 and EECS 227AT
Tentative schedule: here
Final exam: Wed 12/18/19, 8-11AM.