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Workshop: CiML 2019: Machine Learning Competitions for All

Design and Analysis of Experiments: A Challenge Approach in Teaching

Adrien Pavao


Over the past few years, we have explored the benefits of involving students both in organizing and in participating in challenges as a pedagogical tool, as part of an international collaboration. Engaging in the design and resolution of a competition can be seen as a hands-on means of learning proper design and analysis of experiments and gaining a deeper understanding other aspects of Machine Learning. Graduate students of University Paris-Sud (Paris, France) are involved in class projects in creating a challenge end-to-end, from defining the research problem, collecting or formatting data, creating a starting kit, to implementing and testing the website. The application domains and types of data are extremely diverse: medicine, ecology, marketing, computer vision, recommendation, text processing, etc. The challenges thus created are then used as class projects of undergraduate students who have to solve them, both at University Paris-Sud, and at Rensselaer Polytechnic Institute (RPI, New York, USA), to provide rich learning experiences at scale. New this year, students are involved in creating challenges motivated by “AI for good” and will create re-usable templates to inspire others to create challenges for the benefit of humanity.

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