
This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decisionmaking and control, with emphasis on numerically tractable problems, such as linear or constrained leastsquares 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 20042006 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.)

Link to UC Berkeley Schedule of classes:
here.
Tentative schedule: here.
Final exam: 5/14/15, 36P.
