Graduate Course "Machine Learning"

Graduate Course "Machine Learning" 2007

This page contains information about the graduate course "Machine Learning", and material for the course. More material will be added as the course progresses.

Lectures

Below you find links to download the slides used in the course.
ML Course - First Session
Introduction (Jan 24, 2007)
ML Course - Lecture 1
Lecture 1 (Jan 26, 2007)
ML Course - Lecture 2
Lecture 2 (Feb 2, 2007)
ML Course - Lecture 3
Lecture 3 (Feb 14, 2007)
ML Course - Lecture 4
Lecture 4 (Mar 2, 2007)
ML Course - Lecture 5
Lecture 5 (Mar 2, 2007)
ML Course - Lecture 6
Lecture 6 (Mar 16, 2007)
ML Course - Lecture 7
Lecture 7 (Mar 30, 2007)
 

Ph.D. Student Seminars


Muhammad Rehan Ahmed
Reinforcement Learning
May 11, 2007

Marcello Cirillo
Bayesian Networks
May 11, 2007

Jörgen Ungh
Independent Component Analysis
June 7, 2007

Magnus Svensson
Boltzmann Machines
June 7, 2007

Jonas Melchert
AdaBoost
June 7, 2007

Master Student Project Presentations


Alexei Borissov
Aminu Imoro
Jakob Janecek
June 7, 2007

Alexei Borissov
Aminu Imoro
Jakob Janecek
June 7, 2007
     

Master Student Projects

Each group (2-3 persons) is required to choose on of each projects from the two categories below, i.e. a classification and a regression task. Two groups may not choose the same (first come first serve).

Classification

  1. Gel Spot
  2. Sugar Beets
  3. Breast Cancer
  4. Thyroid

Regression

  1. Process Cooling
  2. Power Load Prediction

Master Student Seminars

Generalization 2 – Vapnik-Chervonenkis Dimension

given by Aminu Imoro, March 29, 10:30 - 12:00 o'clock, 2007
Material for the seminar:

Generalization 1 – Bias and Variance, Regularization, Training with Noise, ...

given by Magnus Malm, March 29, 10:30 - 12:00 o'clock, 2007
Material for the seminar:

RPROP (Resilient PROPagation)


Markus Ingvarsson, Mar 12, 2007

Material for the seminar:
  • M. Riedmiller, Heinrich Braun, "A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm", Proc. of the IEEE Intl. Conf. on Neural Networks, pp. 586 - 591, 1993

Multi-Layer Perceptron, 2:nd Order Learning Algorithms


Jakob Janecek, Mar 12, 2007

Material for the seminar:
  • Chapter 4.10 from the book "Neural Networks for Pattern Recognition" by Christopher Bishop

Multi-Layer Perceptron, 1:st Order Learning Algorithms


Alexei Borissov, Mar 12, 2007

Material for the seminar:
  • Chapter 4 (- 4.9 inclusively) from the book "Neural Networks for Pattern Recognition" by Christopher Bishop

An Application of kNN Classification


Magnus Malm, Feb 26, 2007

Material for the seminar:
  • H. Ganster*, A. Pinz, R. Röhrer, E. Wildling, M. Binder, and H. Kittler, "Automated Melanoma Recognition", IEEE Transactions on Medical Imaging, 20:3, March 2001

An Application of Decision Trees

Aminu Imoro, Feb 26, 2007
Material for the seminar:
  • M. Chen, A. Zheng, J. Lloyd, M. Jordan, E. Brewer, "Failure Diagnosis Using Decision Trees", Proceedings of the International Conference on Autonomic Computing (ICAC’04)
    [PDF]

Nonlinear Regression


Markus Ingvarsson, Feb 26, 2007

Material for the seminar:
  • Chapter 1 from the book "Nonlinear Regression" by Seber and Wild

Logistic Regression


Jakob Janecek, Feb 12, 2007
Material for the seminar:
  • The full book "Machine Learning, Neural and Statistical Classification"
Only chapter 3 "Classical Statistical Methods" is relevant for this seminar.

LMS Algorithm


Aminu Imoro, Feb 12, 2007
Material for the seminar:
  • Least-Mean-Square Algorithm (Haykin's book "Neural Networks – A Comprehensive Foundation", Section 3.5)

Probability Density Estimation


Magnus Malm and Markus Ingvarsson, Feb 12, 2007
Material for the seminar:
  • Probability Density Estimation (Chapter 2 from Christopher Bishop's book "Neural Networks for Pattern Recognition")
The sections that are most important for the seminar are section 2.1, 2.2 and 2.5.

Classification Issues


Alexei Borissov, Feb 12, 2007
Material for the seminar:
  • Chapter 1 of the book "Pattern Recognition" by Richard Duda, Peter Hart, David Stork

Perceptron


Jakob Janecek, Feb 1, 2007
Material for the seminar:
  • Decision Theory (Chapter 1 - 3 from the book "An Introduction to Neural Netwoks" by Ben Kröse and Patrick van der Smagt)
Note that it's mainly chapter 3 which is important for the seminar.

Decision Theory


Alexei Borissov, Feb 1, 2007
Material for the seminar:
  • Decision Theory (Chapter 36 from David MacKay's book "Information Theory, Inference, and Learning Algorithms")
    [PDF]
  • Statistical Decision Theory (Slides following the book "Pattern Recognition" by Richard Duda, Peter Hart, David Stork)
    [PDF]

External Links