EECS127/227AT

Optimization Models and Applications

Instructors: A. Bayen, L. El Ghaoui

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, such as linear or constrained least-squares optimization. 

The course covers two main topics: practical linear algebra and convex optimization.The image on the left 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.)

To communicate: We do not use this site to communicate, post homeworks, etc. We use bCourses, and Piazza for student-GSI discussions.

Link to UC Berkeley Schedule of classes: EECS 127 and EECS 227AT.

Tentative schedule: here.

Practical information: here.

Final exam: 12/14/18, 8-11AM.