Supervised Machine Learning: Applications, Opportunities, and Challenges
Machine learning (ML) algorithms have become popular in business, industry, and technology over the last few decades. This presentation will provide an overview of the developments and applications, with a focus on supervised learning methods that are applied in finance and banking. We will then describe opportunities and challenges as well as some of the ongoing research to mitigate the challenges. In particular, we will discuss the need for interpretability of ML results and recent developments on the use of inherently-interpretable ML algorithms.
Dr. Vijay Nair is the Head of the Advanced Technologies for Modeling of Wells Fargo. He is currently working in quantitative analytics and risk modeling, leading a team in statistical learning and high-performance computing. He is also the D. A. Darling Professor Emeritus of University of Michigan. He was a Professor of Statistics and Industrial and Operations Engineering at University of Michigan, Ann Arbor for 23 years. He served as Chair of the Statistics Department from 1998-2010. Before joining University of Michigan, he was a research scientist at Bell Labs Math Sciences Center for 15 years. He is an elected Fellow of the American Association for the Advancement of Science, American Statistical Association, American Society for Quality, and Institute of Mathematical Statistics. He is a former President of the International Statistical Institute, former (chief) editor of International Statistical Review, and former editor of Technometrics. He has supervised 24 PhD students from IOE and Statistics Departments.