Presentation by Emily Fox, Amazon Professor of Machine Learning, UW at the 2018 GeekWire Cloud Tech Summit: geekwire.com/cloudsummit
Most of the recent success stories in machine learning involve a clear prediction goal combined with a massive (benchmark) training dataset. However, many practical machine learning problems do not fit this mold. In this talk, I am going to focus primarily on learning from time-series data, discuss some open challenges beyond prediction, and present paths forward to handle limited data scenarios and notions of interpretability in deep learning models.
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