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Tutorial Program
The tutorials are scheduled for Thursday, September 8, 2005.
We have selected the following two high quality tutorials for the IDA 2005 participants.
Tutorial 1: Probabilistic Inductive Logic Programming
Luc De Raedt and Kristian Kersting
Probabilistic inductive logic programming (PILP), sometimes also
called statistical relational learning, addresses one of the central
questions of artificial intelligence: the integration of probabilistic
reasoning with first order logic representations and machine
learning. A rich variety of different formalisms and learning
techniques have been developed.
This tutorial provides an introduction to and an overview of the
state-of-the-art in statistical relational learning. We start from
classical settings for inductive logic programming, namely learning
from entailment, learning from interpretations, and learning from
proofs, and show how they can be extended with probabilistic
methods. While doing so, we review state-of-the-art statistical
relational learning approaches and show how they fit the discussed
learning settings for probabilistic inductive logic programming.
For more information on this tutorial, look
here.
Bio Sketch
Luc De Raedt received his Ph.D. (Dr. Informatica) in Computer Science
from Katholieke Universiteit Leuven (Belgium) in 1991. He worked at
the Department of Computer Science of the Katholieke Universiteit
Leuven from 1986 till 1999. Since April 1999 he is a Professor at the
Albert-Ludwigs-Universitaet Freiburg and head of the Machine Learning
and Natural Language Processing Lab research group. He is/has been
coordinating several EU project on ILP and probablistic ILP, was/is
program (co)-chair of the ECML94, ILP95, ECML/PKDD01, and ICML-05,
serves/served on numerous program committees (including IJCAI, ECAI,
SIGKDD, ECML, ICML, AAAI, ILP ) and editorial boards (including JAIR,
JMLR, MLJ, IDA, ...).
Kristian Kersting received his M.Sc. (Diplom) in Computer science from
the Albert-Ludwigs-Universtaet Freiburg (Germany) in 2000. He is
currently finishing his Ph.D. on "Probabilistic Logic Learning". He
was actively involved in the developement of Bayesian Logic Programs,
Logical Hidden Markov Models, and recently on a theory of relational
reinforcement learning. His research interests are centered around
Probabilistic Logic Learning and Machine Learning, as well as their
applications. Together with James Cussens, he gave a tutorial on
Probabilistic Logic Learning at ICML 2004. He served as program
committee member for DS, ICML, SRL, MRDM, and ACM-SAC.
Tutorial 2: Statistical Bases of Machine Learning
B. Apolloni and D. Malchiodi
Machine Learning represents the new deal of statistical inference once
powerful computational tools have been made available to scientists.
The objects we want to infer are not yet simple parameters but entire
functions. The data we process are not simple independent observations
of a phenomenon; rather they represent complex links between different
variables characterizing it. This tutorial provides a statistical
framework for perceiving, discussing and solving the key inference
problems on which a large family of machine learning instances are
rooted.
Moving from the elementary problem of estimating the parameter of a
Bernoulli variable, we will revisit two basic inference tools: the
computation of confidence intervals and the search for point estimators
with nice properties. Then we will go on to learning problems: while
the theoretical tools remain unchanged, the sample properties to be
twisted on the population must be wisely devised and smartly computed.
Bio Sketch
Bruno Apolloni received his degree in Mechanical Engineering from the
Università degli Studi di Napoli, Italy, in 1969. He is a full
professor in Computer Science and teaches Cybernetics and Information
Theory at the Dipartimento di Scienze dell'Informazione of the
University of Milano, Italy, head of the Neural Networks Research
Laboratory (LAREN) of the above department
and President of the Italian Society of Neural Networks (SIREN).
Dario Malchiodi received a degree in Computer Science in 1996 and a
Ph.D. in Applied Mathematics in 2000, both from the University of
Milano, Italy. He is currently assistant professor at the same
University, where he teaches Computer Programming and Foundations of
Computer Science. His main research area ranges from probability theory
and mathematical statistics to various aspects of computational
learning theory.
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