GALILEO GALILEI FOUNDATION
WORLD FEDERATION OF SCIENTISTS
ETTORE MAJORANA CENTRE FOR SCIENTIFIC CULTURE
GALILEO GALILEI CELEBRATIONS
Four Hundred Years Since the Birth of MODERN SCIENCE
INTERNATIONAL SCHOOL ON NEURAL NETS
"E.R. CAIANIELLO"
1st Course: LEARNING IN GRAPHICAL MODELS
A NATO Advanced Study Institute
ERICE - SICILY: 27 SEPTEMBER - 7 OCTOBER 1996
Sponsored by the:
- European Union
- International Institute for Advanced Scientific Studies (IIASS)
- Italian Institute for Philosophical Studies
- Italian Ministry of Education
- Italian Ministry of University and Scientific Research
- Italian National Research Council (CNR)
- Sicilian Regional Government
- University of Salerno
PROGRAMME AND LECTURERS
Introduction to graphical models (directed and undirected graphs)
Inference (probabilistic propagation, junction trees, conditioning)
Properties of conditional independence (Markov properties, separation)
Chain graphs
Mixture models, hidden Markov models, decision trees
Neural networks
Data structures for efficient estimation (bump trees, ball trees)
Bayesian methods
Structure learning (metrics, search, approximations)
Priors
Statistical mechanical methods (decimation, mean field)
Markov chain Monte Carlo (importance sampling, Gibbs sampling, hybrid MC)
Bayesian graphical models (BUGS software)
Learning and phase transitions
Clustering and multidimensional scaling
Model selection and averaging
Surface learning and family discovery
Online learning
Causality
- J. WHITTAKER, University of Lancaster, UK
- D. MADIGAN, University of Washington, Seattle, WA, USA
- D. GEIGER, Technion, Haifa, Israel
- U. KJAERULLF, Aalborg University, Denmark
- R. COWELL, University College, London, UK
- M. STUDENY, Academy of Sciences, Czech Republic
- S. OMOHUNDRO, NEC Research, Princeton, NJ, USA
- D. HECKERMAN, Microsoft Research, Redmond, WA, USA
- G. COOPER, University of Pittsburg, PA, USA
- W. BUNTINE, Thinkbank, USA
- L. SAUL, MIT, Cambridge, MA, USA
- J. BUHMANN, University of Bonn, Germany
- N. TISHBY, Hebrew University, Israel
- D. MACKAY, University of Cambridge, UK
- D. SPIEGELHALTER, MRC, Cambridge, UK
- J. PEARL, University of California, Los Angeles, CA, USA
PURPOSE OF THE COURSE
Neural networks and Bayesian belief networks are learning and interface
methods that have been developed in two largely distinct research communities.
The purpose of this Course is to bring together researchers from these two
communities and study both kinds of networks as instances of a general
unified graphical formalism. The Course will focus on probabilistic methods
for learning in graphical models, with attention paid to algorithm analysis
and design, theory and applications.
Group Photo
DIRECTORS OF THE COURSE: D. HECKERMAN - M.I. JORDAN
DIRECTORS OF THE SCHOOL: M.I. JORDAN - M. MARINARO
DIRECTOR OF THE CENTRE: A. ZICHICHI