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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

Subjects

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)

Schedule 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

Topics Title Classification An Introduction Handout Slides Classification Nearest Neighbor and Linear Classification Handout Slides

Literature

Supervisor

Student

Johannes Osterholzer

Eva Brumme

Johannes Osterholzer

Jakob Kruse

literature.pdf: [3] (2.1-2.5, 2.7-2.8, 3.1-3.5)

literature1.pdf: [1] (14.1-14.2) literature2.pdf: [7] (4.1)

Regression Handout Slides

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

Clustering Essay Slides

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 Essay Slides

literature1.pdf: [3] (6.1-6.5, 6.7) literature2.pdf: [1] (15.1-15.4)

Torsten Stüber

Tobias Nett

Neural Networks Essay Slides

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 Essay Slides

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 Essay Slides

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 Essay Slides

literature1.pdf: [3] (9) literature2.pdf: [4] (6.1-6.4) (additional reading) literature3.pdf: [6] (5.1)

Torsten Stüber

Ismail Ilkan Ceylan

Torsten Stüber

Alena Iakina

Torsten Stüber

Arezoo Kashefi

Torsten Stüber

Alina Petrova

Toni Dietze

Carl-Phillip Hänsch

Genetic Algorithms Essay Slides

literature.pdf: [5] (12)

Reinforcement Learning Essay Slides

literature1.pdf: [3] (18) literature2.pdf: [5] (13)

Feature Selection Essay Slides Binarization of Synchronous Context-Free Grammars Essay Slides

[11]

[10] (1-4)

Literature 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.

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