Course Schedule - Spring Semester 2020

     

COMP 545 001 (CRN: 23123)

ADV TOPICS IN OPTIMIZATION

Long Title: ADVANCED TOPICS IN OPTIMIZATION: FROM SIMPLE TO COMPLEX ML SYSTEMS
Department: Computer Science
Instructor: Kyrillidis, Tasos
Meeting: 2:30PM - 3:45PM TR DCH 1075 (13-JAN-2020 - 24-APR-2020) 
Session: Full Term
Grade Mode: Satisfactory/Unsatisfactory
Course Type: Seminar
Language of Instruction: Taught in English
Method of Instruction: Face to Face
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
 
Restrictions:
Must be enrolled in one of the following Level(s):
Graduate
Section Max Enrollment: 50
Section Enrolled: 15
Enrollment data as of: 22-JUN-2021 7:48AM
 
Additional Fees: None
 
Final Exam: GR Course-Dept Schedules Exam
 
Description: COMP 545 is a graduate-level course on optimization techniques and algorithms, as these are used in modern ML/AI/SP tasks. During this course, we will learn and study the above topics (both in depth and breadth). The course i) will focus on different objective classes (convex vs. non-convex objectives, with constraints or not, etc.), ii) will cover different optimization strategies within each class, iii) will study algorithmic choices based on computational resources (e.g., use of low-dimensional structures (when/why), asynchronous vs. synchronous algorithms, distributed algorithms, etc.) and iv) lastly, will study schemes that handle some specific, but well-spread optimization constraints (sparsity, low-rankness).The main objective of the course is to highlight optimization as a vital part of contemporary research in ML/AI/SP, and draw the attention of students to open-questions in related topics. In particular, the aim for students is to i) learn how to distinguish differences in research papers of related fields, ii) understand the connection between them and how researchers advance each area, and iii) be able to consider possible extensions of these works, as part of the final (open-ended) project of the course. Repeatable for Credit.