Basic Information
- Course Code: SC 646
- Course Name: Distributed Optimization and Machine Learning
- Course Offered In: 2022-‘23
- Semester Season: Spring
- Instructors: Mayank Baranwal
- Prerequisites: Proficiency in Systems Theory at the level of SC625/SC301 or equivalent, as recommended by the Syscon department. Familiarity with EE302 is sufficient for preparation, as suggested based on experience.
- Difficulty (1 being easy and 5 being tough): 3
Course Content
Content : Review of convex optimization:
Properties of convex functions Iterative algorithms for constrained and unconstrained optimization First and second-order methods Dynamical systems viewpoint
Review of control theory: Lyapunov theory Single and double-integrator systems
Review of graph theory: Elements of graph theory Network topology Multi-agent systems
Distributed optimization problem: Problem formulation Distributed consensus Algorithms for distributed optimization Dynamical Systems viewpoint Robustness
Applications: Distributed economic dispatch Distributed control of UAVs
Machine learning: Distributed clustering Distributed training of neural networks Federated learning
Feedback on Lectures
The lectures were excellent, characterized by the professor’s engaging and interactive board-style teaching approach. Encouraging questions and discussions, the professor adeptly covered the entire course content. Homework problems were thoughtfully designed as an extension of class concepts. Additionally, the professor’s consideration for a brief break during the 90-minute lectures was appreciated, adding to the overall positive experience of the sessions.
Feedback on Evaluations
The course comprised three homework assignments and a single end-of-term examination. Homework tasks typically expanded upon or applied the material covered in class. Originally intended as a viva-style assessment, the final evaluation was modified to an hour-long exam due to the large class size. The exam questions closely resembled the interactive nature of a viva session and focused on topics discussed in the lectures.