Stochastic Approximation: Theory and Applications

ABOUT THE COURSE: Stochastic approximation refers to a class of iterative algorithms that can locate zeroes or optimal points of functions in scenarios where the function evaluations are compromised by noise. These algorithms are prominently utilized in regression, system identification, adaptive control, and increasingly in reinforcement learning and machine learning. This course will explore the design, theoretical convergence, and convergence rates of these algorithms, with a particular emphasis on applications in reinforcement learning INTENDED AUDIENCE: UG, Masters and Ph.D. students in Computer Science and Engineering/Electronics and Communication Engineerings/Mathematics PREREQUISITES: Real analysis, Measure-theoretic Probability, Optimization, Design and Analysis of Algorithms, Ordinary Differential Equations INDUSTRY SUPPORT: Google Research, Microsoft Research, Adobe Research