Les infos clés
This course introduces students to the real world challenges of implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, KNN and regression trees and how to apply them to actual stock trading situations.
This course is composed of three _mini-courses_: - Mini-course 1: Manipulating Financial Data in Python - Mini-course 2: Computational Investing - Mini-course 3: Machine Learning Algorithms for Trading Each mini-course consists of about 7-10 short lessons. Assignments and projects are interleaved. **Fall 2015 OMS students**: There will be two tests - one midterm after mini-course 2, and one final exam.
- Tucker Balch - Tucker is a former USAF F-15 pilot and current professor of Interactive Computing at the Georgia Institute of Technology. Dr. Balch’s research centers on Machine Learning. He teaches courses in multi-robot systems, Artificial Intelligence and Finance. Balch has published over 120 research publications. His work has been covered by CNN, Institutional Investor, the Wall Street Journal and the New York Times. Balch earned a Bachelor’s degree at Georgia Tech, a Master’s degree at UC Davis, and a Ph.D. at Georgia Tech. His graduated students work at NASA JPL, CMU, Uber, Goldman Sachs, Morgan Stanley, Citadel, AQR, and Yahoo! Finance.
Udacity est une entreprise fondé par Sebastian Thrun, David Stavens, et Mike Sokolsky offrant cours en ligne ouvert et massif.
Selon Thrun, l'origine du nom Udacity vient de la volonté de l'entreprise d'être "audacieux pour vous, l'étudiant ". Bien que Udacity se concentrait à l'origine sur une offre de cours universitaires, la plateforme se concentre désormais plus sur de formations destinés aux professionnels.