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Fri Dec 9th 08:00 AM -- 06:30 PM @ Room 129 + 130
Challenges in Machine Learning: Gaming and Education
Isabelle Guyon · Evelyne Viegas · Balázs Kégl · Ben Hamner · Sergio Escalera

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Challenges in machine learning and data science are competitions running over several weeks or months to resolve problems using provided datasets or simulated environments. The playful nature of challenges naturally attracts students, making challenge a great teaching resource. For this third edition of the CiML workshop at NIPS we want to explore more in depth the opportunities that challenges offer as teaching tools. The workshop will give a large part to discussions around several axes: (1) benefits and limitations of challenges to give students problem-solving skills and teach them best practices in machine learning; (2) challenges and continuous education and up-skilling in the enterprise; (3) design issues to make challenges more effective teaching aids; (4) curricula involving students in challenge design as a means of educating them about rigorous experimental design, reproducible research, and project leadership. CiML is a forum that brings together workshop organizers, platform providers, and participants to discuss best practices in challenge organization and new methods and application opportunities to design high impact challenges. Following the success of last year's workshop (, in which a fruitful exchange led to many innovations, we propose to reconvene and discuss new opportunities for challenges in education, one of the hottest topics identified in last year's discussions. We have invited prominent speakers in this field. We will also reserve time to an open discussion to dig into other topic including open innovation, coopetitions, platform interoperability, and tool mutualisation.

08:00 AM Welcome
Evelyne Viegas
08:30 AM Gathering common sense knowledge: how to game it?
Larry Zitnick
09:10 AM The Michigan Data Science Team: A Student Organization for Machine Learning Challenges
Jonathan C Stroud
09:30 AM Energy generation prediction: Lessons learned from the use of Kaggle in Machine Learning Course
Jesus Fernandez-Bes
09:50 AM Learning to improve learning: ML in the classroom
Emma Brunskill
11:00 AM Challenges in education
Balázs Kégl, Ben Hamner
12:00 PM Lunch, posters and discussions
02:00 PM OpenML in research and education
Joaquin Vanschoren
02:40 PM ImageCLEF 2017 LifeLog task
Duc Tien Dang Nguyen
03:30 PM Evaluation-as-a-Service: a serious game
Henning Mueller
04:10 PM Reproducible Research: moving to the BEAT
Sébastien Marcel
04:50 PM CAFA: a Challenge Dedicated to Understanding the Function of Biological Macromolecules
Pedja Radivojac
05:10 PM Interactive Machine Learning (iML): a challenge for Game-based approaches
Andreas Holzinger
05:30 PM Gaming challenges and encouraging collaborations
Sergio Escalera, Isabelle Guyon