There exists a stark difference between today’s machine learning methods and the lifelong learning capabilities of humans. Humans learn many different functions and skills, from diverse experiences gained over many years, from a staged curriculum in which they first learn easier and later more difficult tasks, retain the learned knowledge and skills, which are used in subsequent learning to make it easier or more effective. Furthermore, humans self-reflect on their evolving skills, choose new learning tasks over time, teach one another, learn new representations, read books, discuss competing hypotheses, and more. This tutorial will focus on the question of how to design machine learning agents with similar capabilities. The tutorial will include research on topics such as reinforcement learning and other agent learning architectures, transfer and multi-task learning, representation learning, amortized learning, learning by natural language instruction and demonstration, learning from experimentation.
Tom Mitchell (Carnegie Mellon University)
Tom M. Mitchell is the Founders University Professor and Interim Dean of the School of Computer Science at Carnegie Mellon University. Mitchell has worked in Machine Learning for many years, and co-founded the ICML conference (with Jaime Carbonell and Ryszard Michalski). Recently, he directed the Never-Ending Language Learning (NELL) project, which operated continuously for over eight years, providing a case study for how to architect never-ending learning systems. Mitchell is a member of the U.S. National Academy of Engineering, a member of the American Academy of Arts and Sciences, and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI).
Partha Talukdar (IISc Bangalore / KENOME)
Partha Talukdar is a faculty member in the Department of Computational and Data Sciences (CDS) at the Indian Institute of Science (IISc), Bangalore. He is also the founder of KENOME, an enterprise Knowledge graph company with the mission to help enterprises make sense of big dark data. Previously, Partha was a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University, working with Tom Mitchell on the NELL project. Partha received his PhD (2010) in CIS from the University of Pennsylvania, working under the supervision of Fernando Pereira, Zack Ives, and Mark Liberman. Partha is broadly interested in Machine Learning, Natural Language Processing, and Cognitive Neuroscience, with particular interest in large-scale learning and inference. Partha is a recipient of IBM Faculty Award, Google’s Focused Research Award, and Accenture Open Innovation Award. He is a co-author of a book on Graph-based Semi-Supervised Learning published by Morgan Claypool Publishers. Homepage: http://talukdar.net
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