Carlos Guestrin

 

 


 

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 DARPA.

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Dec 12th 2016

Case Studies: Analyzing Sentiment & Loan Default Prediction
In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

Average: 8 (3 votes)
Dec 12th 2016

Case Study - Predicting Housing Prices
In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.

Average: 7 (3 votes)
Dec 12th 2016

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies.

Average: 5.4 (8 votes)
Dec 12th 2016

Case Studies: Finding Similar Documents. A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover?

Average: 8.7 (3 votes)