Laboratory of Computational Neuroscience |
EPFL - Graduate Course (Master program):Pattern Classification and Machine Learning |
This course focuses on the problem of classifying patterns into several categories. The theory of supervised learning in artificial neural networks will be compared with statistical pattern recognition and approaches from the theory of generalization. The course is jointly taught by W. Gerstner and M. Seeger.
In addition to a series of interactive computer exercises in JAVA that is available for this course, we also offer a Miniproject.
The authorative course page is in Moodle .
Overview:
I. Introduction.
II. Artificial Neural Networks
III. Optimal decisision boundaries and density estimation
IV. Related Classical and Modern Methods
V. Statistical Learning Theory (taught by M. Hasler); see this page
TEXTBOOK
R.O. Duda and P.E. Hart and D.G. Stork: Pattern Classification (Wiley)
OR
S. Haykin: Neural Networks (Prentice Hall)
OR
C. Bishop: Neural Networks for Pattern Recognition (Oxford)
AND
V. Vapnik: The Nature of Statistical Learning Theory (Springer)