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Chair of Foundations of Programming

Seminar Machine Learning in the winter term 2012/2013

About Machine Learning

Machine Learning, a branch of artificial intelligence, investigates approaches for automatically extracting knowledge from example data. In particular, a machine learning system is intended to discover universal patterns in the set of learning data in order to allow for general predictions. Machine learning is an active area of research with a broad range of applications; it is currently and will be of fundamental importance in the future development of intelligent systems.

This is an introductory seminar: we will deal with basic aspects of machine learning.

General Information

Subjects General methods of machine learning such as
  • Supervised learning: classification and regression
  • Unsupervised learning: clustering and dimensionality reduction
  • Reinforcement learning
Audiences committed students who study
  • Bachelor Informatik, Master Informatik, or Diplom Informatik (PO 2004 or PO 2010), and who want to take a Proseminar or Hauptseminar
  • Master Computational Logic, and who want to take a seminar
Prerequisites in this seminar we will deal with basic approaches of machine learning; no prerequisites required
Requirements for passing
  • on your own initiative and on the due date, make appointments with your supervisor (at least 1 week in advance) and hand in the required material
  • (not for Proseminar) a seminar essay of 12–15 pages, complete with title, author, introduction (1 page min.), complete references, self-contained regarding notions and notations, examples and illustrations; of this essay, a preliminary version: complete regarding content, but rudimentary in presentation
  • (Proseminar) Hand-out 1–2 pages (just one sheet!)
  • (everybody) Talk of 30 minutes, supported with the use of suitable media: slides, black board, transparencies, hand-out etc.; of everything, a preliminary version: complete regarding content, but rudimentary in presentation
  • presence at all talks, active participation at the discussions
  • for inclusion into module examination: survey knowledge of the seminar contributions (core statements)


Datum Ereignis
October 10, 2012, 2.DS, INF/E005 introductory meeting and assignment of topics in room INF/3027
until October 31, 2012 first meeting with your supervisor; aim: read literature and make a concept for your essay
until November 23, 2012 hand in preliminary version of essay, arrange appointment with your supervisor
until December 14, 2012 (Hauptseminar and Students of Computational Logic) hand in final version of essay
(Proseminar) hand in handouts
until January 4, 2013 hand in preliminary version of slides, arrange appointment with your supervisor
until January 14, 2013 hand in final version of slides
January 23/24, 2013 (see below) Presentations in INF/3027

Timetable of talks

23 January 24 January
introduction 08:00-08:10 09:30-09:40
talk 08:10-08:50 --------
talk 08:50-09:30 09:40-10:20
talk 09:30-10:10 10:20-11:00
break 10:10-10:20 11:00-13:00
talk 10:20-11:00 13:00-13:40
talk 11:00-11:40 13:40-14:20
break 11:40-13:00 14:20-14:30
talk 13:00-13:40 14:30-15:10
talk 13:40-14:20 15:10-15:50


Title Literature Supervisor Student
    An Introduction
literature.pdf: [3] (2.1-2.5, 2.7-2.8, 3.1-3.5) Johannes Osterholzer Eva Brumme
    Nearest Neighbor and Linear Classification
literature1.pdf: [1] (14.1-14.2)
literature2.pdf: [7] (4.1)
Johannes Osterholzer Jakob Kruse
literature1.pdf: [1] (17.1-17.2)
literature2.pdf: [3] (4.6-4.8)
literature3.pdf: [7] (3.1)
additional reading: [2] (2)
Toni Dietze Alexander Burkhardt
literature1.pdf: [7] (9.1-9.2)
(additional reading) literature2.pdf: [3] (7)
(additional reading) literature3.pdf: [4] (9.1-9.3)
Torsten Stüber Lukas Schweizer
Dimensionality Reduction
literature1.pdf: [3] (6.1-6.5, 6.7)
literature2.pdf: [1] (15.1-15.4)
Torsten Stüber Tobias Nett
Neural Networks
literature1.pdf: [3] (11.1-11.7, 11.10-11.11)
literature2.pdf: [5] (3)
Torsten Stüber Alejandro Alvarez
Graphical Models
    Belief Networks and Markov Networks
literature1.pdf: [1] (3.1, 3.3.1-3.3.5)
literature2.pdf: [1] (4.1, 4.2.1-4.2.2, 4.2.4-4.2.5)
literature3.pdf: [3] (16.1, 16.2, 16.4, 16.6)
Torsten Stüber Kerstin Gößner
Graphical Models
    Inference and Training
literature1.pdf: [7] (8.4.1-8.4.4)
literature2.pdf: [1] (5.1.1-5.1.2)
literature3.pdf: [1] (9.3)
literature4.pdf: [1] (10.1-10.2)
additional reading: [1] (3.1, 3.3, 4.2)
Torsten Stüber Dirk Weißenborn
Decision Trees
literature1.pdf: [3] (9)
literature2.pdf: [4] (6.1-6.4)
(additional reading) literature3.pdf: [6] (5.1)
Torsten Stüber Ismail Ilkan Ceylan
Genetic Algorithms
literature.pdf: [5] (12) Torsten Stüber Alena Iakina
Reinforcement Learning
literature1.pdf: [3] (18)
literature2.pdf: [5] (13)
Torsten Stüber Arezoo Kashefi
Feature Selection
Torsten Stüber Alina Petrova
Binarization of Synchronous Context-Free Grammars
[10] (1-4) Toni Dietze Carl-Phillip Hänsch


Some downloads only work from within the university network.

[1] Barber, D. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.
[2] Zielesny, A. From Curve Fitting to Machine Learning. Springer-Verlag, 2011.
[3] Alpaydin, E. Introduction to Machine Learning. MIT press, 2004.
[4] Nilsson, N.J. Introduction to Machine Learning. unpublished, 1998. download
[5] Marsland, S. Machine Learning: an Algorithmic Perspective. Chapman & Hall, 2009.
[6] Michie, D. and Spiegelhalter, D.J. and Taylor, C.C. and Campbell, J. Machine Learning, Neural and Statistical Classification. Ellis Horwood London, 1994.
[7] Bishop, C.M. Pattern Recognition and Machine Learning. Springer-Verlag, 2006
[8] Weber, C. and Elshaw, M. and Mayer, N.M. (editors) Reinforcement Learning: Theory and Applications. I-Tech Education and Publishing, 2008. download
[9] Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Recovery 2(2):121-167, 1998.
[10] Huang, L. and Zhang, H. and Gildea, D. and Knight, K. Binarization of synchronous context-free grammars. Comput. Linguist. 35(4):559-595, 2009. download
[11] Guyon, I. and Elliseeff, A. An introduction to variable and feature selection. Journal of Machine Learning Research. 3:1157-1182, 2003. download

Getting Help

We have some information on writing articles available online. In general, if you have questions, do not hesitate to contact your supervisor. The earlier you address your problems, the easier the solutions will be.

Last modified: 25th Jan 2013, 10.18 AM
Author: Dr. rer. nat. Torsten Stüber

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