Introduction to the world of Reinforcement Learning: Applications and Challenges  

Reinforcement learning is becoming a very hot area of research, with plenty of success stories coming from multiple and diverse domains.

In this talk, Professor B. Ravindran will introduce the audience to the crux of what reinforcement learning is and why it’s a distinct learning paradigm as compared to the traditional machine learning approaches.

He will also cover the different kind of applications where reinforcement learning can be applied, and the challenges industries are facing in applying RL.


Structure of the Talk:


  • Introduction to Reinforcement Learning
  • Industry applications of RL/case studies around RL
  • Design of a reinforcement learning solution
  • A learning path for a beginner interested in learning RL



SPEAKER



Prof. Balaraman Ravindran


Prof. Ravindran is the head of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI) at IIT Madras and a professor in the Department of Computer Science and Engineering. He is also the co-director of the reconfigurable and intelligent systems engineering (RISE) group at IIT Madras, which has nearly 80 members associated with it currently. He received his PhD from the University of Massachusetts, Amherst He has nearly two decades of research experience in machine learning and specifically reinforcement learning. He has held visiting positions at the Indian Institute of Science, Bangalore, India and University of Technology, Sydney, Australia. Currently, his research interests are centred on learning from and through interactions and span the areas of data mining, social network analysis, and reinforcement learning.

He is one of the founding executive committee members of the India chapter of ACM SIGKDD and is currently serving as the vice-president of the chapter. He has published nearly 100 papers in journals and conferences, including premier venues such as ICML, AAAI, IJCAI, ICDM, ICLR, NIPS, UAI, ISMB, and AAMAS. He has also co-authored the chapter on reinforcement learning in the Handbook of Neural Computation published by Oxford University Press. He has been on the program committees of several premier conferences as well as served as the program co-chair of PAKDD in 2010 and the General co-chair of the 2015 Big Data Summit at Sydney.

He has been closely collaborating with various industrial research labs, such as Ericsson R&D, KLA Tencor, Adobe Research, IBM India Research Labs, Yahoo! Labs and General Motors, working on applications of data mining and machine learning techniques to hard real-world problems. He received Yahoo! Faculty research gifts in 2009 and 2014 to work on mining real-world text data and unrestricted research gifts from KLA Tencor in 2014, 2015 and 2017. He also serves on the advisory boards of several startups in the data analytics and AI space.


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