Matrix is located on the 8th floor of Barrows Hall, on the UC Berkeley campus, near Telegraph and Bancroft Avenues, just up the hill from Sather Gate. There are entrances at both ends of the building, but only one of the elevators on the eastern side goes directly to the 8th floor. You can alternatively take the stairs to the 7th floor and walk up the stairs.
Social scientists are increasingly taking advantage of machine learning methods to gain new insight into their data and expand their methodological toolbox. Indeed, these methods and techniques are revolutionary and indispensable tools for exploring data, learning more deeply about relationships between variables, and ultimately uncovering and visualizing latent or hidden structure embedded in data. This course covers both supervised and unsupervised machine learning methods, but will place special emphasis on the (often) underappreciated suite of unsupervised learning tools. These methods are more exploratory in nature, and include cluster analysis, mixture modeling, principal and independent component analysis, manifold learning and multidimensional scaling, self-organizing maps, factor analysis and structural equation modeling, and other latent variable models. Social scientists have also contributed greatly to the development and innovation of these methods, and special care will be given to integrate social science perspectives and applications into the course materials.
Presented as part of the ICPSR Summer Program in Quantitative Methods of Social Research. Instructor: Christopher Hare, University of California, Davis. To register and for further information, visit this page or contact Eva Seto, Associate Director at Social Science Matrix, at email@example.com.