Registration and practical issues
Machine learning has been developing as a research topic with many applications and
ramifications to other fields during the past few years. This has led to seeing the research
activities in Machine Learning organised by major conferences, journals and networks.
Today, having a background in Machine Learning is a major asset for students, whether to
apply for academic or industrial positions. On the other hand very little focused attention has
been played on the question of how these students were to receive this background. Important
questions such as the following have only received local attention:
- At what level should machine learning be taught?
- What are the experiments? Who is teaching machine learning?
- Due to the many links with mathematics, algorithmics, applications (linguistics,
biology, ...), but also to the strong experimental component, who should the targeted
students be? What background should they have?
- What material (books, software, benchmarks, video-lectures, world wide web) can we
find today for teaching machine learning?
The NOE PASCAL 2, following on initiatives like the machine learning summer schools, the
analysis of patterns schools and the PASCAL bootcamp, proposes to help coordinate the
energies over this question.
The aim of the TML '08 workshop is to bring together researchers and practitioners that are
interested in the teaching and education aspect of the field. A particular attention will be kept
for the Masters level, which is felt to be the possible place where leverage is best and where
the tools that can be provided today by the EC in order to build collaborative courses over
different countries are best suited.
Researchers, practitioners and educators are invited to contribute to and participate in the
The workshop will address issues specific to Teaching Machine Learning including, but not
- Innovative approaches to learning and teaching ML
- Approaches for improving the students' learning experience (Undergraduate and/or Graduate)
- Incorporating ML research into ML courses
- The integration of theory and practice
- Tools for supporting teaching and learning ML
- ML applications to assist in class instruction
The deadline for submitting papers is the 31st of March.
Please send a six-page abstract (including figures and references) to firstname.lastname@example.org
Final versions of the papers will have to use the proposed templates
(based on ICML2008).
Deadline for submission: March 31st
Notification of acceptance: April 10th
Camera-ready copy due: April 30th
Workshop: May 6-7(Lunch), Saint-Etienne-France
The above dates are firm
- Jose Balcázar, Universitat Politècnica de Catalunya, Barcelona
David Barber, University College London, London
Nello Christianini, University of Bristol
Ricard Gavaldá, Universitat Politècnica de Catalunya, Barcelona
Marko Grobelnik, Jožef Stefan Institue, Ljubljana
Mark Herbster, University College London, London
Samuel Kaski, Helsinki University of Technology , Helsinki
Dunja Mladenic, Jožef Stefan Institue, Ljubljana
Luisa Micó, Universidad de Alicante, Alicante
Gunnar Rätsch, Max Planck institute, Tübingen
Bernhard Schölkopf, Max Planck Institute, Tübingen
Marc Sebban, University of Saint-Étienne
John Shawe-Taylor, University College London, London
From University of Saint-Étienne:
- Colin de la Higuera
There are two steps for registration:
- Please send a mail to email@example.com with a short
paragraph expressing your interest in the theme
Upon receipt of a password, register online.
There is no registration fee if registration is completed before the 15th of April.
Registration should be done online at this address
The workshop will be located in the
Laboratoire Hubert Curien, a new lab specialising in Optics, image
processing and machine learning in Saint-Étienne.
You can get to Saint-Étienne by plane (to Lyon Saint-Exupery
airport), by train or by car. Indications as how to get to the lab can
be found on this webpage.