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 General methods of machine learning such as Supervised learning: classification and regression Unsupervised learning: clustering and dimensionality reduction Reinforcement learning
committed students who study Audiences
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)
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
Topics Title Classification An Introduction Handout Slides Classification Nearest Neighbor and Linear Classification Handout Slides
literature.pdf:  (2.1-2.5, 2.7-2.8, 3.1-3.5)
literature1.pdf:  (14.1-14.2) literature2.pdf:  (4.1)
Regression Handout Slides
literature1.pdf:  (17.1-17.2) literature2.pdf:  (4.6-4.8) literature3.pdf:  (3.1) additional reading:  (2)
Clustering Essay Slides
literature1.pdf:  (9.1-9.2) (additional reading) literature2.pdf:  (7) (additional reading) literature3.pdf:  (9.1-9.3)
Dimensionality Reduction Essay Slides
literature1.pdf:  (6.1-6.5, 6.7) literature2.pdf:  (15.1-15.4)
Neural Networks Essay Slides
literature1.pdf:  (11.1-11.7, 11.10-11.11) literature2.pdf:  (3)
Graphical Models Belief Networks and Markov Networks Essay Slides
literature1.pdf:  (3.1, 3.3.1-3.3.5) literature2.pdf:  (4.1, 4.2.1-4.2.2, 4.2.4-4.2.5) literature3.pdf:  (16.1, 16.2, 16.4, 16.6)
Graphical Models Inference and Training Essay Slides
literature1.pdf:  (8.4.1-8.4.4) literature2.pdf:  (5.1.1-5.1.2) literature3.pdf:  (9.3) literature4.pdf:  (10.1-10.2) additional reading:  (3.1, 3.3, 4.2)
Decision Trees Essay Slides
literature1.pdf:  (9) literature2.pdf:  (6.1-6.4) (additional reading) literature3.pdf:  (5.1)
Ismail Ilkan Ceylan
Genetic Algorithms Essay Slides
literature.pdf:  (12)
Reinforcement Learning Essay Slides
literature1.pdf:  (18) literature2.pdf:  (13)
Feature Selection Essay Slides Binarization of Synchronous Context-Free Grammars Essay Slides
Literature Some downloads only work from within the university network. 
Barber, D. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.
Zielesny, A. From Curve Fitting to Machine Learning. Springer-Verlag, 2011.
Alpaydin, E. Introduction to Machine Learning. MIT press, 2004.
Nilsson, N.J. Introduction to Machine Learning. unpublished, 1998. download
Marsland, S. Machine Learning: an Algorithmic Perspective. Chapman & Hall, 2009.
Michie, D. and Spiegelhalter, D.J. and Taylor, C.C. and Campbell, J. Machine Learning, Neural and Statistical Classification. Ellis Horwood London, 1994.
Bishop, C.M. Pattern Recognition and Machine Learning. Springer-Verlag, 2006
Weber, C. and Elshaw, M. and Mayer, N.M. (editors) Reinforcement Learning: Theory and Applications. I-Tech Education and Publishing, 2008. download
Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Recovery 2(2):121-167, 1998.
 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  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.