Credit points (Kreditpunkte):  (Seminar) 3,  (Practical Course) 6

Black board

• Report subsmission deadline: March 15th, 2004. Any report submitted later will not be considered.
• Demo sessions will take place on Thursday February 12th, 2004, 16 - 18 o'clock and Friday 13th February, 2004, 11-13 o'clock.

Index

[Basic Talks]

[Advanced Talks]

[Demo Sessions]

[Background material]

Basic Talks

Date	Authors	Title	PPT	PDF
16.10.2003	L. De Raedt, K. Kersting	Probabilistic Logic Learning	[.ppt, 1120kb]	[.pdf.gz, 1224kb]
	L. De Raedt, K. Kersting. Probabilistic Logic Learning. In SIGKDD Explorations, special issue on Multi-Relational Data Mining, S. Dzeroski and L. De Raedt, editors, Vol. 5(1), pp. 31-48, July 2003. See SIGKDD Explorations.
30.10.2003	D. Zhang	Bayesian Networks	[.ppt, 411kb]	[.pdf.gz, 365kb]
	I. S. Thon	Learning Bayesian Networks	---	[.pdf.gz, 365kb]
	F.V. Jensen. Bayesian Networks and Decision Graphs. Springer, New York, 2001. D. Heckerman. A Tutorial on Learning with Bayesian Networks. In Learning in Graphical Models, M. Jordan, ed.. MIT Press, Cambridge, MA, 1999. Also appeared as Technical Report MSR-TR-95-06, Microsoft Research, March, 1995. An earlier version appears as Bayesian Networks for Data Mining, Data Mining and Knowledge Discovery, 1:79-119, 1997. J. Pearl. Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 2. edition, 1991.
06.11.2003	J. Pastrana	Hidden Markov Models	[.ppt, 876kb]	[.pdf, 614kb]
	L. R. Rabiner and B. H. Juang. An introduction to hidden Markov models. IEEE ASSP Magazine, pages 4-15, January 1986. L. R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE,77(2):257-286,1989.
	T. Guerel	Reinforcement Learning	[.ppt, 361kb]	[.pdf, 1.183kb]
	Leslie Pack Kaelbling, Michael L. Littman, and Andrew W. Moore.Reinforcement learning: A survey. Journal of Artificial Intelligence Research, vol. 4, pp. 237--285, 1996. See JAIR. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998, A Bradford Book.
13.11.2003	D. Meier, A. Kimmig	Probabilistic Context-free Grammars	[.ppt, 3.675kb]	[.pdf, 860kb]
	Chapter 11&12 in C. D. Manning, H. Schuetze.Foundations of Statistical Natural Language Processing. MIT, 1999. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological Sequence Analysis: Probabilistic models of proteins and nucleic acids. Cambridge University Press. 1. edition. 1998
20.11.2003	U. Dick	Tools for Graphical Models	[.ppt, 1054kb]	[.pdf, 860kb]
	Kevin Murphy. Introduction to Graphical Models. 2001. Unpublished. [.pdf] Kevin Murphy. The Bayes Net Toolbox for Matlab. Computing Science and Statistics, vol 33, 2001. [.ps.gz] Kevin Murphy's list of tools for graphical models. web supplements of C. Borgelt and R. Kruse's book "Graphical Models: Methods for Data Analysis and Mining"John Wiley & Sons, Chichester, United Kingdom 2002. gR: the gRaphical models in R project. Hugin Netica
	L. Salaur	Inductive Logic Programming	---	[.pdf, 137kb]
	3rd chapter in, S. Dzeroski and N. Lavrac, Relational Data Mining , Springer-Verlag, 2001. S. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20):629-679, 1994.
27.11.2003	O. Basegmez	HMMs for Information Extraction	[.ppt, 598kb]	[.pdf, 313kb]
	Dayne Freitag and Andrew McCallum. Information Extraction with HMM Structures Learned by Stochastic Optimization. Proceedings of AAAI-2000. Dayne Frietag and Andrew McCallum. Information Extraction with HMMs and Shrinkage. Workshop notes of the AAAI'99 Workshop on Machine Learning for Information Extraction Kristie Seymore, Andrew McCallum, Roni Rosenfeld. Learning Hidden Markov Model Structure for Information Extraction. Workshop notes of the AAAI'99 Workshop on Machine Learning for Information Extraction.
	R. Ramakrishnan	Link Mining	[.ppt, 178kb]	[.pdf, 369kb]
	L. Getoor. Link Mining, SIGKDD Explorations, Volume 5, Issue 1, July 2003. L. Getoor, E. Segal, B. Taskar, D. Koller. Probabilistic Models of Text and Link Structure for Hypertext Classification, IJCAI Workshop on "Text Learning: Beyond Supervision", Seattle, WA, August 2001.
	K. Kersting	Round up and Dicussion of Basic Approaches	---	---

Advanced Talks

Date	Authors	Title	PPT	PDF
18.12.2003	L. Salaur	Type I Logics: Stochastic Relational Models	---	[.pdf, 522kb]
	Corresponding chapters in: L. Getoor. Learning Statistical Models from Relational Data, Ph.D. Thesis, Stanford University, December, 2001. Motivation: J. Halpern. An analysis of first-order logics of probability, Artificial Intelligence 46, 1990, pp. 311-350.
08.01.2004	I. S. Thon	Probabilistic Relational Models	---	[.pdf, 1162kb]
	L. Getoor, N. Friedman, D. Koller, A. Pfeffer. Learning Probabilistic Relational Models, chapter in Relational Data Mining, S. Dzeroski and N. Lavrac, Eds., Springer-Verlag, 2001 N. Friedman, L. Getoor, D. Koller, A. Pfeffer. Learning Probabilistic Relational Models. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), Stockholm, Sweden (July 1999). The full learning approach in detail can be found in: L. Getoor. Learning Statistical Models from Relational Data, Ph.D. Thesis, Stanford University, December, 2001.
	O. Basegmez	Reference and Existence uncertainty within PRMs	[.ppt, 236kb]	[.pdf, 348kb]
	L. Getoor, N. Friedman, D. Koller, B. Taskar. Learning Probabilistic Models of Link Structure. Journal of Machine Learning Research, 2002.
15.01.2004	R. Ramakrishnan	Advanced Link Mining	[.ppt, 186kb]	[.pdf, 388kb]
	L. Getoor. Link Mining, SIGKDD Explorations, Volume 5, Issue 1, July 2003. References [24, 37, 43, 45] in the above article.
	J. Pastrana	Relational (Hidden) Markov Models	[.ppt, 220kb]	[.pdf, 486kb]
	K. Kersting, T. Raiko, S. Kramer, L. De Raedt. Towards Discovering Structural Signatures of Protein Folds based on Logical Hidden Markov Models. In R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung and T. E. Klein, editors, Proceedings of the Pacific Symposium on Biocomputing (PSB-2003), Kauai, Hawaii, USA, January 3-7, 2003. C. Anderson, P. Domingos, D. Weld. Relational Markov Models and their Application to Adaptive Web Navigation, Proceedings of the Eighth International Conference on Knowledge Discovery and Data Mining (pp. 143-152), 2002. Edmonton, Canada: ACM Press.
22.01.2004	D. Meier	PRISM	[.ppt, 232kb]	[.pdf, 283kb]
	Sato, T. and Kameya, Y. PRISM: A symbolic-statistical modeling language. Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI97), pp.1330--1335, 1997. Sato, T. and Kameya, Y. Parameter Learning of Logic Programs for Symbolic-Statistical Modeling. Journal of Artificial Intelligence Research (JAIR), Vol.15, pp.391--454, 2001.
	A. Kimmig	Stochastic Logic Programs	[.ppt, 310kb]	[.pdf, 1.4mb]
	J. Cussens. Parameter estimation in stochastic logic programs. Machine Learning, 44(3):245-271, 2001. J. Cussens. Loglinear models for first-order probabilistic reasoning. In Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 126-133, San Francisco, CA, 1999. Morgan Kaufmann Publishers.
29.01.2004	D. Zhang	Bayesian Logic Programs	[.ppt, 187kb]	[.pdf, 170kb]
	K. Kersting, L. De Raedt. Towards Combining Inductive Logic Programming and Bayesian Networks. In C. Rouveirol, M. Sebag, editors, Proceedings of the Eleventh International Conference on Inductive Logic Programming (ILP-2001), LNAI 2157, Springer, Strasbourg, France, September 2001. K. Kersting, L. De Raedt. Adaptive Bayesian Logic Programs. In C. Rouveirol, M. Sebag, editors, Proceedings of the Eleventh International Conference on Inductive Logic Programming (ILP-2001), LNAI 2157, Springer, Strasbourg, France, September 2001. Corresponding technical reports no. 151 and no. 174, Institute for Computer Science, University of Freiburg, Germany.
	U. Dick	Dynamic PRMs
	S. Sanghai, P. Domingos, D. Weld. Dynamic Probabilistic Relational Models. Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (pp. 992-997), 2003. Acapulco, Mexico: Morgan Kaufmann.
	T. Guerel	Relational Reinforcement Learning	[.ppt, 1.2mb]	[.pdf, 557kb]
	S. Dzeroski, L. De Raedt, K. Driessens. Relational Reinforcement Learning. Machine Learning 43(1/2): 7-52 (2001) K. Kersting, L. De Raedt. Logical Markov Decision Programs. In L. Getoor and D. Jensen, editors, Working Notes of the IJCAI-2003 Workshop on Learning Statistical Models from Relational Data (SRL-03), pp. 63-70, August 11, Acapulco, Mexico, 2003. M. van Otterlo. Relational Representations in Reinforcement Learning: Review and Open Problems Proceedings of the ICML'02 Workshop on Development of Representations, 2002. M. van Otterlo. Reinforcement Learning for Relational MDPs. Accepted for publication at the Annual Machine Learning Conference of Belgium and the Netherlands, 8-9 jan 2004 in Brussels, Belgium.

Demo Sessions

Date	Authors	Title	PPT	PDF
12.02.2004, 16-18 o'clock	R. Guetlein	PCFGs
	A. Cocora	Bayes' Ball	---	[.pdf, 292kb]
	B. Gutmann	Approx. Inference	---	[.pdf, 68kb]
13.02.2004, 11-13 o'clock	R. Schmidt	PRMs	[.ppt, 133kb]	[.pdf, 197kb]
	R. Renner	SRMs	[.ppt, 428kb]	[.pdf, 207kb]
	R. Mattmueller	Magic Sets	---	[.pdf, 142kb]

Further background material

• SIGKDD Explorations, special issue on Multi-Relational Data Mining, S. Dzeroski and L. De Raedt, editors, Vol. 5(1), pp. 31-48, July 2003.See SIGKDD Explorations.


• Probabilistic-Logical Model Repository.