EECS 127 / 227AT: Optimization Models and Applications — Spring 2019
 |
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:
{}{raw} <div align=“center”> <iframe src=“https:calendar.google.comcalendarembed?src=eecs127class40gmail.com&ctz=America2FLos_Angeles” style=“border: 0” width=“800” height=“600” frameborder=“0” scrolling=“no”><iframe> <div>
Syllabus: here.
Midterm 1: 02/26/19, in class.
Midterm 2: 04/11/19, in class.
Final exam: 05/16/19, 8-11AM.