- My textbook Learning from Data is Amazon's #1 bestseller in Machine Learning semi-continuously, and even Amazon's #1 in all categories of Computer Science repeatedly.
- My latest Scientific American article Machines that think for themselves, has been translated into a dozen languages, including Spanish, Italian, Arabic, and Chinese.
- My online course (MOOC) on machine learning has attracted more than 200,000 participants since its launch as Caltech's first-ever live broadcast of a course. Also featured on edX.
Yaser S. Abu-Mostafa is a Professor of Electrical Engineering and Computer Science at the California Institute of Technology. His main fields of expertise are machine learning and computational finance. He is the author of Amazon's machine learning bestseller Learning from Data. His MOOC on machine learning has attracted more than 200,000 participants.
Dr. Abu-Mostafa received the Clauser Prize for the most original doctoral thesis at Caltech. He received the ASCIT Teaching Awards in 1986, 1989 and 1991, the GSC Teaching Awards in 1995 and 2002, and the Richard P. Feynman prize for excellence in teaching in 1996. He was the founding Program Chairman of the annual conference on Neural Information Processing Systems (NIPS), and a founding member of the IEEE Neural Networks Council. He chaired the second and fourth international conferences on Neural Networks in the Capital Markets (NNCM-94 and NNCM-96), and the sixth international conference on Computational Finance (CF-99). In 2005, the Hertz Foundation established a perpetual graduate fellowship named the Abu-Mostafa Fellowship in his honor.
Dr. Abu-Mostafa currently serves on a number of scientific advisory boards, and has served as a technical consultant on machine learning for several companies, including Citibank for 9 years. He has numerous technical publications including 3 articles in Scientific American, as well as several keynote lectures at international conferences.
- Machine Learning
- Computational Finance
- Pattern Recognition and Data Mining
- Information and Complexity
- Foundations of Probability and Statistics