STANFORD COURSE
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.
Access the 10 Stanford Machine Learning Lectures here.
Use the recommended software program: Free Access GNU Octave is a high-level language, primarily intended for numerical computations mostly compatible with Matlab.
Professor Andrew Ng
Assistant Professor
Computer Science Department
Department of Electrical Engineering (by courtesy)
Stanford University
Room 156, Gates Building 1A
Stanford, CA 94305-9010
Tel: (650)725-2593
FAX: (650)725-1449
email: ang@cs.stanford.edu
Stanford University Channel on YouTube:
http://www.youtube.com/stanford
