Machine Learning Your Customers

Development, Mobile
Location: 1A14 Level:
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As data becomes more abundant and ubiquitous thanks to an entire planet of data-generating users, machine learning is becoming a common tool for services. Tony Jebara is the Chief Scientist of Sense Networks, a company whose business model depends on making “sense” of a constant stream of cellphone data to predict consumer activity. This talk will survey the uses of machine larning and how to get started.

Machine learning is the marriage of two fields. From statistics, it inherits powerful inference methods and probability theory. From computer science, it borrows data structures, efficient algorithms and combinatorics. More recently, learning has also benefited from graph theory which has produced state-of-the-art tools. We will explore a suite of current graph-based machine learning tools including clustering, classification, visualization, collaborative filtering, graphical modeling, matching and semisupervised classification. These tools let us do things like visualize massive datasets, social networks and other behavioral data from millions of people. We can ask business relevant questions from passive measurements, such as, what applications will this user be interested in, are they a student or a business traveler, and which other users would they be interested in networking with? With enough training data, otherwise simple machine learning algorithms can do remarkable things.

In the 1950’s artificial intelligence (AI) emerged as the potential holy grail for solving complex human-level tasks ranging from face recognition to playing chess. After decades of trial and error, AI scientists realized that it was hard to manually derive the necessary computational rules for machines to accomplish intelligent tasks. Instead, a far better approach emerged: have the scientists collect many examples of the task to be solved and let the computer figure out the governing rules. This approach to AI is now widely known as machine learning.

Tony Jebara is Associate Professor of Computer Science at Columbia University and director of the Columbia Machine Learning Laboratory. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in vision, networks, spatio-temporal data, and text. Jebara is also co-founder of Sense Networks. He has published over 60 peer-reviewed papers in conferences and journals including NIPS, ICML, UAI, COLT, JMLR, CVPR, ICCV, and AISTAT. He is the author of the book Machine Learning: Discriminative and Generative. Jebara is the recipient of the Career award from the National Science Foundation. His work was recognized with a best paper award at the 26th International Conference on Machine Learning, a best student paper award at the 20th International Conference on Machine Learning as well as an honorable mention from the Pattern Recognition Society in 2000. Jebara’s research has been featured on television (ABC, BBC, New York One, TechTV, etc.) as well as in the popular press (New York Times, Slash Dot, Wired, Businessweek, IEEE Spectrum, etc.). He obtained his PhD in 2002 from MIT. Recently, Esquire magazine named him one of their Best and Brightest of 2008. Jebara’s lab is supported in part by the NSF, CIA, NSA, DHS, and ONR.

Tony Jebara

Sense Networks Inc.

r. Jebara is Director of the Columbia University Machine Learning Laboratory, whose research focuses upon machine learning, computer vision and related application areas such as human-computer interaction. Jebara is also a Principal Investigator at Columbia’s Vision and Graphics Center.

He has published over 50 peer-reviewed papers in conferences and journals including NIPS, ICML, UAI, COLT, JMLR, CVPR, ICCV, AISTAT, and PAMI. He is the author of the book Machine Learning: Discriminative and Generative (Kluwer).

Jebara is recipient of the Career Award from the National Science Foundation and has also received honors for his papers from the International Conference on Machine Learning and from the Pattern Recognition Society. He has served as chair, program committee member, and reviewer for various conferences and workshops.

Jebara’s research has been featured on television (ABC, BBC, New York One, TechTV) as well as in the popular press (Wired Online, Scientific American, Newsweek, and Science Photo Library).

Jebara obtained his Bachelor’s degree from McGill University (at the McGill Center for Intelligent Machines). He obtained a Master’s and Ph.D. from the Massachusetts Institute of Technology (at the MIT Media Laboratory).

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