MACHINE LEARNING

In the seminar Machine Learning the following topics will be covered:

• A short introduction to the general state of the art in Machine Learning

• The perceptron and related models (e.g. multi-layers feed-forward nets)

• An introduction to stochastic dynamics

• A general theory for constructing learning rules

• The (restricted) Boltzmann machine learning and its equivalence to Hebbian learning

All of these topics can be found in the book Theory of Neural information Processing Systems, by A.C.C. Coolen, R. Khuen, P. Sollich. The participants of the seminar are recommended to obtain a copy of this book prior to participating.

DETAILED DESCRIPTION


instructor: ELENA AGLIARI, SAPIENZA UNIVERSITY

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Elena obtained a master degree in Condensed Matter Physics (2004) and a Ph.D. degree in Theoretical Physics (2007) at the University of Parma, Italy. She has been working at the Albert-Ludwigs-Universitaet of Freiburg (Germany) and at the Laboratoire de Physique Theorique de la Matiere Condensee, Universite Pierre et Marie Curie, Paris (France); she is currently researcher in Applied Mathematics in Sapienza University, Rome. Her research interests include complex systems, statistical mechanics, with particular focus on neural networks, machine learning and biological complexity- but she is also active in the fields of graph theory and in stochastic processes. She has got the Italian scientific qualification as professor for both Theoretical and Mathematical Physics. She is (co-)author of about 80 publications on international scientific journals (edited by Nature Publishing Group, American Physical Society, Institute of Physics Publishing, etc). An extended CV, with all the details and all the produced papers, is available at her website: www.elenaagliari.weebly.com. Prospective participants can email her directly if they have any questions about the seminar.