Review: Machine Learning Applications to Internet of Things [Slides]

The review of the TEWI colloquium of Dr. Hari Prabhat Gupta from June 22, 2018 comprises the slides (below).


Internet of Things (IoT) is growing rapidly in decades, various applications came out from academia and industry. IoT is an amazing future to the Internet, but there remain some challenges to IoT for human have never dealt with so many devices and so much amount of data. Machine Learning (ML) is the technique that allows computers to learn from data without being explicitly programmed. Generally, the aim is to make predictions after learning and the process operates by building a model from the given (training) data and then makes predictions based on that model. Machine learning is closely related to artificial intelligence, pattern recognition and computational statistics and has strong relationship with mathematical optimization. In this talk, we focus on ML applications to IoT. Specially, we focus on the existing ML techniques that are suitable for IoT. We also consider the issues and challenges for solving the IoT problems using ML techniques.


Dr. Hari Prabhat Gupta received the B.E. degree in Computer Engineering from Government Engineering College Ajmer, Ajmer, India, the M.Tech. and Ph.D degrees in Computer Science and Engineering from the Indian Institute of Technology Guwahati (IITG), Guwahati, India. He worked with Samsung R&D Bangalore, India. He has received a research fellowship from TATA Consultancy Services, India. He is currently working as Assistant Professor in Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India. His research interests include wireless sensor networks, wireless ad hoc networks, and distributed algorithms. He has published various IEEE and ACM conference papers and Journals in the field of wireless sensor networks.

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