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.)
Link to UC Berkeley Schedule of classes:
Tentative schedule: here.
Final exam: There will be a final exam, TBD.