| MACHINE LEARNING AND DATA MINING
August 2014 - July 2015
In the era of BigData, Machine Learning has emerged as the leading discipline for building statistical models from data emanating from heterogenous sources. Over the next year we plan to hold several workshops to understand foundational, algorithmic and applied aspects of Machine Learning. The year will begin with a Symposium on the interplay between Algorithms, Complexity theory and Computational Learning theory. With the advent of Internet, Learning Complex networks has received a new impetus. An workshop is being planned on this theme, which will examine the fundamental questions and their practical implications in addressing real problems. Reinforcement Learning remains an important discipline of Machine Learning, which attempts to design autonomous systems which can learn by interacting with the external world. An workshop will be organized to learn about recent advances in this important area. Recent advances in Non-convex optimization can throw light on many Unsupervised Learning Problems. A summer school will be organized to review the fundamentals and discuss recent advances. Driven by inverse problems arising in Signal Processing, Sparsity has emerged as an important criterion in learning representations. To understand the basic results and modern advances an interdisciplinary workshop on Learning Sparse representations for Signal Processing is being planned.