ECGR 5115: Convex Optimization and AI Applications
University of North Carolina, Charlotte
This course focuses on the theory and the algorithmic aspects of convex optimization. Topics include�convex sets, convex functions, and convex optimization problems; duality theory and optimality conditions;�algorithms for solving convex problems; use of numerical tools to solve problems; and if time permits, we�will discuss non-convex optimization problems.�Throughout this course, we will discuss applications of the materials to engineering topics (as time�permits) such as signal processing, control, robotics, machine learning, statistics, and related engineering�problems.�Credit will not be given for ECGR 5115 where credit has been given for ECGR 4115.
Average GPA: 3.79
Grade distribution records: 27 students across 3 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 20 | 74.1% |
| B | 3 | 11.1% |
| C | 1 | 3.7% |
| W | 3 | 11.1% |
Based on 27 student grade records across 3 terms and 2 professors.
Instructors
- Ahmed Arafa 18 students, Average GPA 3.69
- Dipankar Maity 9 students, Average GPA 4.00