Machine learning for algebraists

  • Sara Jamshidi Zelenberg (Illinois Institute of Technology, Chicago, USA)
E1 05 (Leibniz-Saal)


In this talk, we present a set of techniques from machine learning relevant to algebraists with potential applications. We provide an overview of some 'best practices' used in other ML domains and provide some sample code. In addition, we demonstrate the potential with some preliminary results of ongoing work with Petrovic and Stasi. Attendees are encouraged to come with their laptops if possible.

01.07.19 04.07.19

Summer School on Randomness and Learning in Non-Linear Algebra

MPI für Mathematik in den Naturwissenschaften Leipzig (Leipzig) E1 05 (Leibniz-Saal)
Universität Leipzig (Leipzig) Felix-Klein-Hörsaal

Saskia Gutzschebauch

Max-Planck-Institut für Mathematik in den Naturwissenschaften Contact via Mail

Paul Breiding

Technische Universität Berlin

Jesus De Loera

University of California at Davis

Despina Stasi

Illinois Institute of Technology

Sonja Petrovic

Illinois Institute of Technology