Instructor: Avishek Ghosh, Assistant Professor, SysCon and C-MInDS, IIT Bombay
Contact: Room 110, SysCon; Email: avishek_ghosh@iitb.ac.in
Timing: Tuesday/Friday 2:00 - 3:25 pm
Classroom: LT 002, Lecture Hall Complex-2
TA: 1) Siddhartha Ganguly (email: sganguly@iitb.ac.in)
2) Souvik Das (email: souvikd@iitb.ac.in)
Scribe Format: Here
Scribe Schedule: Here
About the course: This is a course on Optimisation algorithms. It is roughly divided in two parts:
Part I (Foundational ): Convergence rates of Convex objective, Gradient Descent (GD), Constraint Optimisation, Algorithms for constraint objectives--Projected GD, Frank-Wolfe. Non-smooth optimisation; Sub-Gradients, Large Scale Sparse Opt., Proximal Methods; Stochastic Optimisation, SGD, Convergence rate of SGD for convex and non-convex objectives
Part II (Advanced): Accelerated Gradient methods, Adaptive Gradients--Adagrad, ADAM, Large Scale Learning: Federated Learning (FL), Federated Averaging, Convergence of Fed-Avg, Challenges in FL, Neural Networks, SGD convergence for NN, Benign Overfitting
Grading: HWs (25%), 1 mid term (25%), Final (30%), Scribe (10%) and Class participation (10%)
References: We won't follow any standard textbook. The following are the references (pdfs of all the following are available online):
For Part I:
1) Optimization for Data Analysis; S. Wright and Ben. Recht (Rough draft available here)
2) Convex Optimization; S Boyd and L Vandenberghe
3) Numerical Optimization; Nocedal and Wright
For Part II:
1) Lectures on Convex Optimization; Y. Nesterov
2) Research Papers (will be announced prior to class)
Apart from these resources, we will follow similar courses like: Convex Optimization (EECS 227C, UC Berkeley) by Prof. Martin J. Wainwright, Theoretical Foundations of Data Science II (DSC 40B, UC San Diego) by Prof. Arya Mazumdar, Convex Optimization (EECS 227C, UC Berkeley) by Prof. Jiantao Jiao to name a few.
Lectures:
Scribes will be uploaded twice--once before the midsem, once before the endsem.
General Guidelines for Homeworks:
Homeworks should be submitted in class. Students are encouraged to discuss among themselves while solving the HW problems. However, they are required to write the solution on their own. Near-identical submissions will not be awarded any score.