Machine learning has the potential to radically improve our ability to learn from the current deluge of data. In this tutorial we will provide an introduction to modern machine learning methods, and we will show how practitioners are using machine learning to detect fraud, analyze social networks, and build personalized recommender services.
Using a series of case studies, we will walk you through the common tasks followed in all applied machine learning problems, from data cleaning, through model building, to predictions and finally insight. Through this applied process we will share our intuition behind the most popular techniques and why they are best for particular applications.
Throughout the tutorial we will show that implementing these methods and analyses is straightforward using GraphLab. GraphLab is like Hadoop for graphs in that it enables users to easily express and execute machine learning algorithms on massive graphs. We will also show how GraphLab leverages Amazon web services, advances in graph representation, asynchronous communication, and scheduling to achieve orders-of-magnitude performance gains over systems like Hadoop on real-world data.
This will be an active tutorial. Participants are encouraged to bring a laptop to walk through the code, execution, and commands used in the case studies. After attending this tutorial you will have the tools you need to analyze massive datasets, create scalable recommendation services, and maybe even change the world.
Carlos Guestrin is the Amazon Professor of Machine Learning at the
Computer Science & Engineering Department of the University of
Washington. He is also a co-founder and CEO of GraphLab Inc.,
focusing large-scale machine learning and graph analytics. His
previous positions include the Finmeccanica Associate Professor at
Carnegie Mellon University and senior researcher at the Intel Research
Lab in Berkeley. Carlos received his PhD and Master from Stanford
University, and a Mechatronics Engineer degree from the University of
Sao Paulo, Brazil. Carlos’ work has been recognized by awards at a
number of conferences and two journals: KDD 2007 and 2010, IPSN 2005
and 2006, VLDB 2004, NIPS 2003 and 2007, UAI 2005, ICML 2005, AISTATS
2010, JAIR in 2007 & 2012, and JWRPM in 2009. He is also a recipient
of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan
Fellowship, IBM Faculty Fellowship, the Siebel Scholarship and the
Stanford Centennial Teaching Assistant Award. Carlos was named one of
the 2008 `Brilliant 10’ by Popular Science Magazine, received the
IJCAI Computers and Thought Award and the Presidential Early Career
Award for Scientists and Engineers (PECASE). He is a former member of
the Information Sciences and Technology (ISAT) advisory group for
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