[intermediate] AI and ML Applications in Finance and Retail
This course will discuss how to develop successful applications of machine learning for use in e-commerce, physical retail, and bond and equity markets. The course will be based on the instructor’s experience leading teams at Amazon in Seattle and at Goldman Sachs in New York.
Machine learning (ML) as an academic research field is over 60 years old. So why is there so much excitement about it nowadays in the business world? What can the technology really do now that was impossible ten years ago? For what applications are humans still fundamentally superior to algorithms? If we want to apply ML in trading or in banking or in retail, where are the best opportunities? What are a dozen different traps to avoid falling intro? How should an applied ML project be directed and organized? Should we use deep learning? These lectures will provide answers to these questions.
For a preview, see https://www.youtube.com/watch?v=F3kngGr5f7c
We will assume knowledge of practical ML algorithms as explained, for example, in https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/
Charles Elkan is the founder and CEO of a fintech company that has not yet been announced publicly. He is also an adjunct professor at the Stern School of New York University, and at the University of California, San Diego, where he was previously a tenured full professor. Until 2020 he was a managing director and the global head of machine learning at Goldman Sachs in New York, while from 2014 to 2018 he was the first Amazon Fellow, leading a team of over 30 scientists and engineers in Seattle, Palo Alto, and New York doing research and development in machine learning for both e-commerce and cloud computing. Professor Elkan earned his doctorate at Cornell University and his undergraduate degree at Cambridge University. His students have gone on to faculty positions at universities that include Columbia and Stanford, and to leading roles in industry that include managing the largest app store in the world.