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Abstract: Computers used to be programmed by humans, but now they program themselves by learning from data. How is this possible? There are five main schools of thought in machine learning, and each has its own master algorithm – a general-purpose learner that can in principle be applied to any domain. The symbolists have inverse deduction, the connectionists have backpropagation, the evolutionaries have genetic programming, the Bayesians have probabilistic inference, and the analogizers have kernel machines. In this talk I will summarize the key ideas in each of these paradigms, give examples of their major applications - from recommender systems to robot scientists - and speculate on how machine learning will transform business, science, and society.
Biography: Pedro Domingos is a professor of computer science at the University of Washington and the author of "The Master Algorithm". He is a winner of the SIGKDD Innovation Award, the highest honor in data science. He is a Fellow of the Association for the Advancement of Artificial Intelligence, and has received a Fulbright Scholarship, a Sloan Fellowship, the National Science Foundation’s CAREER Award, and numerous best paper awards. His research spans a wide variety of topics in machine learning, artificial intelligence, and data science, including scaling learning algorithms to big data, maximizing word of mouth in social networks, unifying logic and probability, and deep
learning.
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