Learning from humans: A broad overview of approaches to model preferences, skills and perception

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• Maria Perez-Ortiz (University of Cambridge)
• Thursday 23 November 2017, 14:15-15:15
• SS03 Meeting Room, Computer Laboratory.

If you have a question about this talk, please contact Alexander Simpson.

In different scenarios machine learning algorithms need to learn from humans. We may want to model preferences to construct a recommender system, determine the skills of players to better design game tournaments, get some insight about our perception of the world or simply because these systems rely on human feedback to interact with a complex environment. This talk will give a broad introduction to some of the experimental strategies and methods from statistics and machine learning that can be used to model human preferences, skills and perception, ranging from the use of Likert scales, mean opinion scores and pairwise comparison experiments, to ranking, ordinal classification and scaling methods.

This talk is part of the Rainbow Group Seminars series.

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