Machine Learning in Finance: Lessons Learned

Speaker: Charles Elkan, University of California, San Diego Abstract: 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? In what ways are humans still fundamentally superior? If we want to apply ML in trading or in banking, where are the best opportunities? What are ten different traps to avoid falling intro? How should an applied ML project be directed and organized? Should we use deep learning? This talk will provide answers, hopefully reasoned, to these questions.