Foundations of Intelligent Systems
Theoretical Foundations of Learning Finite State Automata 2 Akihiro YAMAMOTO
This course examines the foundations of Machine Learning. The first part explains Computational Learning Theory, which is for learning from discrete data, such as formal languages, and formulae of first-order logic.
The second part explains Statistical Learning Theory, which is for learning from numerical and continuous data. Both parts consist of fundamental theories and methods of their applications.
- Graduate School of Informatics
- Instructor name
- Akihiro YAMAMOTO（Professor）
Marco CUTURI（Associate Professor）
Dipak Kalra, Ph.D., Stanley M. Huff, M.D.
Graduate School of Informatics2014
Graduate School of Informatics2013
Graduate School of Informatics2008