[vc_row][vc_column width=”1/2″][vc_column_text]IEEE Young Professionals Affinity Group in the Montreal chapter in collaboration with IEEE Control Systems Society (CSS) Young Professionals
Date: Tuesday, August 25th, 12-1 pm EST (Montreal Time)
Title: Interactive Machine Learning in Control with Azure ML and Kaggle platform Application
Speaker: Dr. S.L. Brunton, University of Washington, USA
Speaker: Dr. R. Lakshmana Kumar, Hindusthan College of Engineering and Technology, Coimbatore, India
Abstract: The fields of machine learning and control theory are rapidly converging, enabling data-driven control design for previously intractable problems. Many tasks in control theory may be posed as optimization problems constrained by dynamics, and this perspective fits naturally with the view that machine learning is a growing set of data-driven optimization and applied regression techniques to build models from data. Thus, machine learning may aid in the design of models and controllers for high-dimensional, nonlinear systems with non-convex optimization landscapes. In this session, we will begin with a brief overview of the fundamentals of ML and the various ways in which ML intersects with control theory. Then, in a hands-on session, Microsoft Azure Machine Learning (Azure ML) will be used along with the Kaggle platform, so participants can learn how ML can be applied to engineering problems, including dynamics and control. This session will enable the audience to leverage their existing data processing and model development skills, and help them scale their workloads to the cloud. To better communicate event details, please preregister here.
Biography: Dr. Steven L. Brunton is an Associate Professor of Mechanical Engineering at the University of Washington. He is also an Adjunct Associate Professor of Applied Mathematics and a Data Science Fellow at the eScience Institute. Steve received a B.S. in mathematics from Caltech in 2006 and the Ph.D. in mechanical and aerospace engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, locomotion, optics, energy systems, and manufacturing. He is a co-author of three textbooks, received the Army and Air Force Young Investigator Program awards, the Presidential Early Career Award for Scientists and Engineers (PECASE), and he was awarded the University of Washington College of Engineering junior faculty and teaching awards.
Biography: Dr. R. Lakshmana Kumar is currently associated with Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu. He is a Chief Research Scientist in a Canadian based company (ASIQC) in the Vancouver region of British Columbia, Canada. He holds the certification in Data Science from John Hopkins University, United States. He also holds the Amazon Cloud Architect certification from Amazon Web Services. He is the Founding Member of IEEE SIG of Big Data for Cyber Security and Privacy, IEEE. He serves as a core member of the Editorial Advisory Board of Artificial Intelligence group in Cambridge Scholars Publishing, UK.
Date: Tuesday, August 25th, 12-1 pm EST (Montreal Time)
Title: IEEE CSS YP: Interactive Machine Learning in Controls
Interested in learning a new skill and working with your fellow young professionals (YP) while doing it? Join the YP event to learn how to use machine learning with controls in an interactive way using the Kaggle platform. Participants will first get an overview of Kaggle, a resource for learning, development, and collaboration used by the machine learning and data science community. Then, YP will work in small groups to create a controller concept virtually within the Kaggle framework. To better communicate event details, please preregister here.[/vc_column_text][/vc_column][vc_column width=”1/2″][vc_single_image image=”3407″ img_size=”full”][/vc_column][/vc_row]