Course Schedule - Spring Semester 2022

     

Meeting location information can now be found on student schedules in ESTHER (for students) or on the Course Roster in ESTHER (for faculty and instructors).
Additional information available here.

ELEC 570 001 (CRN: 25774)

DISTRIBUTED OPT AND ML

Long Title: DISTRIBUTED METHODS FOR OPTIMIZATION AND MACHINE LEARNING
Department: Electrical & Computer Eng.
Instructor: Uribe, Cesar
Meeting: 4:00PM - 5:15PM TR (10-JAN-2022 - 22-APR-2022) 
Part of Term: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
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: 30
Section Enrolled: 9
Enrollment data as of: 12-JUN-2024 7:35PM
 
Additional Fees: None
 
Final Exam: GR Course-Dept Schedules Exam
 
Description: This course will provide a comprehensive presentation of modern design and analysis methods for distributed and decentralized algorithms for signal processing, optimization, control and machine learning applications. The course will focus on mathematical analysis techniques for the iteration, computational and communication complexity of distributed data processing methods over networks, where data is generated, stored or processed by groups of computational units or agents connected via communication channels over networks. The aim is to introduce modern approaches for distributed information processing with a deep understanding on the effects of communication constraints, network topology, computational resources, and robustness. The contents of this course lie in the intersection of network science, optimization and machine learning. Topics will cover the classical literature in distributed decision making, opinion dynamics, distributed optimization, decentralized control, to more recent topics in distributed machine learning, federated learning, and social learning. Recommended Prerequisite(s): Linear Algebra, Probability Theory, Nonlinear Optimization, Numerical Analysis