Emily Fox

 

 


 

Emily B. Fox received the S.B. degree in 2004, M.Eng. in 2005, E.E. in 2008, and Ph.D. in 2009 from the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). She is currently an assistant professor in the Statistics Department at the University of Washington, and was formerly at the Wharton Statistics Department at the University of Pennsylvania. Her Ph.D. was advised by Prof. Alan Willsky in the Stochastic Systems Group, and from 2009-2011 she was a postdoc in the Department of Statistical Science at Duke University working with Profs. Mike West and David Dunson. Emily is a recipient of the Sloan Research Fellowship, ONR Young Investigator award, NSF CAREER award, National Defense Science and Engineering Graduate (NDSEG) Fellowship, National Science Foundation (NSF) Graduate Research Fellowship, and NSF Mathematical Sciences Postdoctoral Research Fellowship. She has also been awarded the 2009 Leonard J. Savage Thesis Award in Applied Methodology, the 2009 MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize, the 2005 Chorafas Award for superior contributions in research, and the 2005 MIT EECS David Adler Memorial 2nd Place Master's Thesis Prize. Her research interests are in large-scale Bayesian dynamic modeling and computations.

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Dec 5th 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 5th 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 5th 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 5th 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)