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Building Machines that Learn from Examples of Complicated Things

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If you have a question about this talk, please contact Sven Friedemann.

Machine Learning is an area at the intersection of Computer Science, Statistics, and Applied Math that aims to build more powerful computer systems by designing programs that can learn from examples. In this talk, I will discuss some of the challenges (and partial solutions) that arise when trying to use machine learning to model highly structured objects like segmentations of images into their constituent parts, paths that people take to work, or even computer programs. The emphasis will be on how we can efficiently and accurately extract the knowledge in our minds and collective intelligence, and how we can build computer systems that leverage this information to do more useful things.

This talk is part of the Darwin College Sciences Group series.

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