The Ultimate Software
Machine Learning and Intelligence
Paul G. Allen School of Computer Science & Engineering
University of Washington
About the Lecture
Machine learning is the automation of discovery. With it, computers can program themselves instead of having to be programmed by us. Learning systems are widely used in science, business and government, but are still shrouded in mystery. This lecture will explain the five major paradigms in machine learning – symbolic learning, deep learning, genetic algorithms, Bayesian learning and reasoning by analogy. And it will provide samples of some of the major applications they enable, from automated biology to personalized recommendations. The lecture will conclude with a look at the future of what machine learning will bring, and roadblocks, dangers, and opportunities on that will come with that future.
About the Speaker
Pedro Domingos is a professor of computer science and engineering at the University of Washington, interested in data science, machine learning and artificial intelligence. He helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks.
Pedro is on the editorial board of Machine Learning, is past associate editor of Journal of Artificial Intelligence Research, and co-founder of the International Machine Learning Society. He is an author on over 200 technical publications and is the author of “The Master Algorithm” a lay book about machine learning and AI. He writes widely on AI and related subjects for the media, including the Wall Street Journal, Spectator, Scientific American and Wired.
Among other honors, Pedro has received the SIGKDD Innovation Award and the IJCAI McCarthy Award, two of the highest honors in data science and AI. He is Fellow of the AAAS and AAAI,
Pedro earned his undergraduate degree from IST of the Technical University of Lisbon in Portugal, and an MS and PhD in Information and Computer Science at UC-Irvine.