Archive for the ‘Machine Learning’ Category

blight3As the Data Science for Social Good fellowship enters its final month, many of the projects with nonprofit organizations and government agencies are picking up momentum. At the DSSG website, we’re posting regular updates on the fellows’ progress: how they determined the right problem to solve, what analytic and software tools they’re using to attack those problems, and what they have learned along the way. Some of the articles even offer a glimpse at early results and prototypes developed by the team over the first two months. Here’s a sampling of those progress reports.

Cook County Land Bank: The Problem

The Cook County Land Bank Authority was recently established earlier this year as a new government agency charged with acquiring and redeveloping vacant and abandoned properties. DSSG fellows are working with The Institute for Housing Studies at DePaul University to developed a tool — a sort of “Trulia for abandoned properties” — that will help the agency determine which properties to purchase in order to produce the greatest benefit for the surrounding community.

The Cook County land bank wants to play the midwife, proactively targetingindividual properties that have redevelopment potential and could help stabilize local areas.

But there are tens of thousands of boarded up homes and overgrown lots in Cook County, and the land bank’s budget is limited. How will the agency figure out which of these properties to acquire, and what to do with them?

Where can it actually step in and be effective, investing in properties that would not otherwise have been redeveloped, instead of soon-to-be-sold or unsellable ones?


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John Lafferty in the Mansueto Library. (Photo by Jason Smith)

Learning a subject well means moving beyond the recitation of facts to a deeper knowledge that can be applied to new problems. Designing computers that can transcend rote calculations to more nuanced understanding has challenged scientists for years. Only in the past decade have researchers’ flexible, evolving algorithms—known as machine learning—matured from theory to everyday practice, underlying search and language-translation websites and the automated trading strategies used by Wall Street firms.

These applications only hint at machine learning’s potential to affect daily life, according to John Lafferty, the Louis Block Professor in Statistics and Computer Science. With his two appointments, Lafferty bridges these disciplines to develop theories and methods that expand the horizon of machine learning to make predictions and extract meaning from data.

“Computer science is becoming more focused on data rather than computation, and modern statistics requires more computational sophistication to work with large data sets,” Lafferty says. “Machine learning draws on and pushes forward both of these disciplines.”


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