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 support vector machines. What we really need, however, is a single algorithm combining the key features of all of them. In this webinar I will summarize the five paradigms and describe my work toward unifying them, including in particular Markov logic networks. I will conclude by speculating on the new applications that a universal learner will enable, and how society will change as a result.
Presenter: Pedro Domingos, University of Washington in Seattle; SIGKDD Innovation Award Winner
Pedro Domingos is a professor of computer science at the University of Washington in Seattle. 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. He received his Ph.D. from the University of California at Irvine and is the author or co-author of over 200 technical publications. He has held visiting positions at Stanford, Carnegie Mellon, and MIT. He co-founded the International Machine Learning Society in 2001. 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.
Moderator: Gregory Piatetsky-Shapiro, KDnuggets; SIGKDD Co-Founder
Gregory Piatetsky-Shapiro is President of KDnuggets, which provides consulting in the areas of business analytics, data mining, data science, and knowledge discovery. Previously, he led data mining and consulting groups at GTE Laboratories, Knowledge Stream Partners, and Xchange. Gregory is also Editor and Publisher of KDnuggets News and the KDnuggets.com website, leading resources for data mining and analytics news, software, jobs, courses, data, education, and more.
Gregory is the founder of Knowledge Discovery in Database (KDD) conferences. He organized and chaired the first three Knowledge Discovery in Databases workshops in 1989, 1991, and 1993, and then chaired the KDD Steering Committee until 1998, when he co-founded ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining. He served as Director (1998-2005) and was elected SIGKDD Chair (2005-2009 term). Gregory has over 60 publications, with over 10,000 citations, including two best-selling books and several edited collections on topics related to data mining and knowledge discovery. Gregory was the first recipient of ACM SIGKDD Service Award (2000). He also received the IEEE ICDM Outstanding Service Award (2007) for contributions to data mining field and community.