Reading Seminar on Algebraic Geometry and Singular Learning Theory


Friday, 10:00 - 12:00

Room: FR 6046


Dr. Franz Király, Dr. Paul Larsen


Singular Learning Theory is the study of singular parametric estimation, where naive application of classical learning and model selection methods like Max-Likelihood, Bayes Learning, AIC or BIC fails. As virtually all meaningful and practically relevant learning machines like Neural Networks, Mixture Models, Hidden Markov Models or Boltzmann Machines are singular, the analysis of their singular properties is of high practical relevance. Sumio Watanabe has developed generalizations of Bayes Learning Theory and Bayes Model Selection for the singular case; the aim of this seminar is the study of his work and its ramifications.


Knowledge in Algebraic Geometry, Singularity Theory, Parametric Statistics and Bayes Estimation Theory is useful, but not necessary; all relevant basics will be discussed in the course.


The topic list refers to the book Algebraic Geometry and Statistical Learning Theory, by Sumio Watanabe.



Discussion leader

20 April 2012

Organizatorial Meeting and Overview on Singular Learning

Duncan Blythe and Paul Larsen

11 May 2012

Chapter 1: Learning Theory and Singular Parametric Models

Franz Király

18 May 2012

No Seminar

25 May 2012

Chapter 1: Singular Examples, Learning and Generalization Error

Franz Király

1 June 2012

Chapter 1: Evidence and the Bayes quartet

Paul Larsen

8 June 2012

No Seminar

15 June 2012

Chapter 1: The Zeta Function

Duncan Blythe

22 June 2012

Algebraic Geometry Basics

IDA Wiki: Main/SS12_AGSLT (last edited 2012-06-09 21:36:31 by FranzKiraly)