[Liste-proml] Deadline extension: Workshop on Teaching Machine Learning at ICML 2012

Kurt Driessens kurt.driessens at gmail.com
Mar 15 Mai 22:37:44 CEST 2012

[Apologies for multiple postings]

                      DEADLINE EXTENTION
                     ICML 2012 Workshop on
                   Teaching Machine Learning
                 Edinburgh, Scotland, June 30th

We've extended the deadline for submissions to May 26st.

A career in academics usually consists of two main branches of work: doing research (and finding the funds to do it) and teaching. While both consume a substantial amount of time of most people attending ICML each year, the machine learning community shares a lot of information about the first branch, but much less about the second.  With this workshop, we offer attendees of ICML the opportunity to share information and experiences about how they (would like to) teach machine learning and what can be learned from teaching machine learning.

* Peter Flach from the Department of Computer Science, University of Bristol.
* Andrew Ng from the Computer Science Department, Stanford University. 
* David Barber from the Department of Computer Science, University College London. 

The primary goal of this workshop is to initiate sharing of information on machine learning courses and projects and make teaching materials available for reuse (by populating a "ML nifty projects" database similar to http://nifty.stanford.edu/). Secondary goals include fostering a discussion on how to introduce the science of machine learning to non-experts and discovering and sharing of new knowledge and experiences about the application of machine learning techniques by non-experts, such as students during machine learning projects.

We welcome submissions on a range of topics related to teaching machine learning including, but not limited to:
- How to teach ML to non-experts? Position papers addressing this question could discuss which topics to cover, what background to start from, which foundation to use, etc. For example, submissions could weigh statistical, probabilistic or algorithmic approaches to teaching machine learning.
- Interesting projects/assignments. Papers in this category should present interesting assignments where interesting could mean: closely related to real world problems, with unexpected results, easily re-usable, highly motivating for students, or that yield an insight into ML algorithms or into their application by non-experts. Sharing experiences on these topics could not only provide the community with new, high quality didactic material, but also provide insight in the application of ML techniques by non-experts and the problems these non-experts encounter when dealing with their first machine learning applications.
- How to include new media in the machine learning courses?  Examples include videolectures, online tutorials, etc.

We welcome short position papers (3-4 pages) or description of "nifty" student assignments or projects. Papers should be formatted using the ICML style-files and submitted by email to: dke-teachingml at maastrichtuniversity.nl.  Accepted submissions will be presented either as plenary presentation or as posters during the workshop.

- Submission Deadline: May 26st 2012
- Notification Date: May 23rd 2012
- Workshop Date: June 30th 2012

Kurt Driessens
Elisa Fromont

dke-teachingml at maastrichtuniversity.nl
Kurt Driessens & Elisa Fromont

Plus d'informations sur la liste de diffusion Liste-proml