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Workshop
Challenges in Machine Learning: Gaming and Education
Isabelle Guyon · Evelyne Viegas · Balázs Kégl · Ben Hamner · Sergio Escalera

Thu Dec 08 11:00 PM -- 09:30 AM (PST) @ Room 129 + 130
Event URL: http://ciml.chalearn.org/ciml2016 »

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 (http://ciml.chalearn.org/), 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.

Author Information

Isabelle Guyon (U. Paris-Saclay & ChaLearn)
Evelyne Viegas (Microsoft Research)
Balázs Kégl (Université Paris Saclay/CNRS)
Ben Hamner (Kaggle)
Sergio Escalera (Computer Vision Center and University of Barcelona)

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