Statistical Learning refers to the application and development of statistical frameworks to problems relating to pattern recognition. It is a growing area of research in Statistics and Computer Science. Recognizing patterns in a set of data, either for prediction or inference purposes, lies at the core of scientific pursuits. Not surprisingly, it has influenced the development of scientific investigation in many fields as Economics, Political Science, Finance, Sociology, Biostatistics, Medicine, Marketing, and many other areas.
Some applications of statistical learning involve inference about the flow of information in a network, classifying unseen text data based on previously classified observations (supervised learning and classification), automatically identifying data with similar underlying features (unsupervised learning and clustering), and so on.
Our goal with this interdisciplinary workshop is to provide a forum for researchers and graduate students share how they have applied statistical learning techniques to solve problems in their disciplines. We hope this workshop can provide an environment that facilitates cross-disciplinary information sharing, aiding graduate students, faculty, and researchers in the pursuit of tools to solve problems in their respective disciplines.
The Statistical Learning Workshop is a Rackham Interdisciplinary Workshop. For more information, see: www.rackham.umich.edu/academics/rii/interdisciplinary-workshops.
Some applications of statistical learning involve inference about the flow of information in a network, classifying unseen text data based on previously classified observations (supervised learning and classification), automatically identifying data with similar underlying features (unsupervised learning and clustering), and so on.
Our goal with this interdisciplinary workshop is to provide a forum for researchers and graduate students share how they have applied statistical learning techniques to solve problems in their disciplines. We hope this workshop can provide an environment that facilitates cross-disciplinary information sharing, aiding graduate students, faculty, and researchers in the pursuit of tools to solve problems in their respective disciplines.
The Statistical Learning Workshop is a Rackham Interdisciplinary Workshop. For more information, see: www.rackham.umich.edu/academics/rii/interdisciplinary-workshops.
Student Coordinators
Patrick Wu (pywu at umich.edu)
Diogo Ferrari (dferrari at umich.edu)
Diogo Ferrari (dferrari at umich.edu)
Faculty Coordinator
Walter Mebane (wmebane at umich.edu)
