Machine Learning: Kernel-based Methods
(Introduction to the Mathematics of Supervised Learning)
Contents
This lecture provides a thorough introduction to kernel-based learning methods.
Prerequisites
Basic knowledge in statistics, analysis, and linear algebra is helpful.
Time
summer semesters (since 2004), Friday, 10.15 s.t.
Handouts (new)
Unit 1: The Learning Problem
Unit 2: Linear Regression and Discrimination
Unit 3: The Perceptron Revisited
Unit 4: Kernels and Reproducing Kernel Hilbert Spaces
Unit 5: Basic Kernel-based Learning Methods
Unit 6: Introduction to Constraint Optimization lecture notes
Unit 7: Support Vector Machines
Unit 8: Solving the SVM Optimization Problem
Unit 9: Rademacher Complexity
Script
Machine Learning: Kernel-based
Methods
Laboratory Course
information
about the laboratory course
Suggested reading
J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern
Analysis. Cambridge University Press, 2004.
N. Cristianini and J. Shawe-Taylor. An
Introduction to Support Vector Machines (and other kernel based
learning methods). Cambridge University Press, 2000.
K. Lang. Optimization.
Springer Texts in Statistics, Springer-Verlag, 2004
T. Poggio and S. Smale. The Mathematics of Learning: Dealing with
Data. Notices of the American Mathematical Society (AMS), 50(5),
2003
T. Hastie, R. Tibshirani, and J. Friedman.
The Elements of Statistical Learning: Data Mining, Inference, and
Prediction.
Springer-Verlag, 2001
C. M. Bishop.
Pattern Recognition
and Machine Learning.
Springer-Verlag, 2006
Adaptive Systems Seminar
Description
Seminar about recent findings in the domain of optimization
of adaptive systems for diploma and doctoral students.
Time
winter/summer semesters (since 2003/2004), Friday, 14.15 s.t.
Reinforcement Learning
Description
The lecture provides an introduction to reinforcement learning (RL).
It covers the basics of RL, including Markov decision processes,
dynamic programming, and temporal-difference learning. Additionally,
special topics such as policy gradient methods as well as the
connection to neural networks and evolutionary computation are
presented.
Prerequisites
Very basic knowledge in statistics and analysis is required,
basic knowledge about neural networks is helpful.
Time
winter semesters (since 2003/2004), Friday, 10.15 s.t.
Slides
Unit 1: Introduction
Unit 2: The Reinforcement Learning
Problem gridworld example
Unit 3: Dynamic Programming
Unit 4: Monte Carlo Methods
Unit 5: Temporal Difference Learning
Unit 6: Eligibility Traces forward/backward proof
Unit 7: Generalization and Function Approximation CMAC details
Unit 8: Planning and Learning
Unit 9: Least-squares Temporal Difference Learning
Unit 10: Policy Gradient Methods
Unit 11: Direct Polcy Search
Laboratory Course
information
about the laboratory course
Additional course material
V. Heidrich-Meisner, M. Lauer, C. Igel, and M. Riedmiller.
Reinforcement Learning in a Nutshell. In M. Verleysen, editor,
15th European Symposium on Artificial Neural
Networks (ESANN 2007), Belgium: d-side publications, pp. 277-288, 2007
Machine Learning: Unsupervised Learning
with Prof. Dr. Laurenz Wiskott, see his pages on the course for details
Description
This course is mainly given by
Laurenz Wiskott. It
covers a variety of unsupervised methods from machine learning such as principal component analysis, independent component analysis, vector quantization, clustering, self-organizing maps, growing neural gas, Bayesian theory and graphical models, deep-belief networks, and Markov random fields.
Time
winter semesters, starting 2009/2010
lecture: Tuesday, 14.15 s.t.
analytical tutorial: Tuesday, 12.15 s.t.
Literature and Lecture Notes
For many topics a script will be available, other literature will be mentioned in the lecture.
German lecture notes on Markov random fields can be found here.
Prerequisites
Good command of linear algebra and calculus.
Neurocomputing Methods
with Prof. Dr. Gregor Schöner
Description
A seminar about fundamental methods in neurocomputing.
Time
just in summer semester 2004