Computer Science 651 - Optimization/Computer Science

Optimization techniques are frequently used in many areas of computer science, in particular in machine learning, to handle a variety of large-scale, data intensive problems. Moreover, algorithmic tools of ever-increasing sophistication are introduced at a fast pace, offering unparalleled opportunities to solve problems efficiently. This class will cover a wide range of optimization methods, including convex, nonconvex and discrete optimization. The first two thirds of the course are dedicated to foundational topics, whereas the last third is dedicated to the study and understanding of cutting-edge techniques presented at leading conferences in the field.

Open to Graduate Computer Science students only. GRADUATE STUDENTS ENTERING THE COURSE ARE EXPECTED TO HAVE A STRONG WORKING KNOWLEDGE OF LINEAR ALGEBRA (MATH 545 EQUIVALENT) AND CALCULUS (EQUIVALENT TO A IN MATH 233), AS WELL AS BASIC KNOWLEDGE OF PROBABILITY AND STATISTICS. SOLID PROGRAMMING SKILLS IN A HIGH-LEVEL LANGUAGE SUCH AS PYTHON, R OR MATLAB ARE EXPECTED. SEATS HELD FOR INCOMING GRAD STUDENT REGISTRATION. STUDENTS NEEDING SPECIAL PERMISSION MUST REQUEST OVERRIDES VIA THE ON-LINE FORM: https://www.cics.umass.edu/overrides.