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Recent years have seen a revival of deep neural networks in machine learning. Although this has lead to impressive reduction in error rates in some prominent machine learning tasks, it also raises the concern about interpretability of machine learning algorithms.

Kilian Weinberger, associate professor of computer science, describes the basics of deep learning algorithms and their building blocks. His talk is part of a series on "The Emergence of Intelligent Machines: Challenges and Opportunities."

Weinberger received his Ph.D. from the University of Pennsylvania in Machine Learning under the supervision of Lawrence Saul, and his undergraduate degree in Mathematics and Computer Science from the University of Oxford. During his career he has won several best paper awards at ICML, CVPR, AISTATS and KDD runner-up award. In 2011 he was awarded the Outstanding AAAI Senior Program Chair Award and in 2012 he received an NSF CAREER award. He was elected co-Program Chair for ICML 2016 and for AAAI 2018. Kilian Weinberger's research focuses on machine learning and its applications. In particular, he focuses on learning under resource constraints, metric learning, machine learned web-search ranking, computer vision and deep learning.