Modern Approaches to Machine Learning
(Winter term; 2h of lectures per week)
This course addresses graduate students in Computer Science
during the initial period of their PhD.
See also the page of the
Graduate School in Computer Science
Objectifs:
Adaptive methods to programming and to `intelligence'
are of increasing importance in Computer Science.
In this course some important approaches are discussed.
Specifically, the course will concentrate on modern concepts
of adaptive intelligence that arise in the context
of supervised learning and machine learning.
Most of these methods have been inspired by
research in neural networks,
but are now recognized as much more general principles
with a wide area of application.
Contents:
The course in Winter 2002/2003 covers the following topics.

Part I. Lectures
 22.10. Introduction: pattern recognition and classification; simple perceptrons
 29.10. Artificial Neural Networks: Multilayer Perceptrons and BackProp
 5.11. Generalization and Regularization
 12.11 Classical statistical approaches to classification
 19.11. Maximum likelihood, mixture models, and Expectation Maximization (EM)
 26. 11. Support Vector Machines (SVM)
 3.12. SVM: Quadratic Programming, Optimization under constraint
 10. 12 Comparison of supervised approaches (Radial basis functions, Gaussian mixture models and Fuzzy Logic).
Introduction to Reinforcement Learning
 17.12. Reinforcement Learning: Bellman equation, Qlearning,
adaptive control
 17.12. Reinforcement Learning: Qlearning in continuous space and time
 Part II. Miniproject
Miniproject :
Comparison of 3 different approaches (EM, BackProp, SVM) on a
data base.
BOOKS:
 C. Bishop, Neural Networks and Pattern recognition,
Oxford, 1995.
 S.Haykin, Neural Networks, Prentice Hall, 1994
 R.O. Duda and P.E. Hart and D.G. Stock, Pattern Classification,
John Wiley, 2001

N. Scholkopf and A.J. Smola, Learning with kernels:
support vector machines, regularization, optimization, and beyond, MIT press,
2002
 Sutton and Barto, Reinforcement Learning, MIT Press.
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