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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.

Student Calendar:

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Syllabus: here.

Midterm 1: 02/26/19, in class.

Midterm 2: 04/11/19, in class.

Final exam: 05/16/19, 8-11AM.