Instructor: Christopher Hare, UC Davis
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. We will also cover the burgeoning subfield of model interpretability, discussing approaches that can be used to better understand the mechanisms underlying “black box” models. Software: The course will use R to demonstrate the theoretical properties and empirical applications of these methods, and so participants should have some basic familiarity with R or similar statistical computing environments (such as Stata, SAS, or Python). An advanced programming background is not required or assumed. Prerequisites: Participants should also have some prior exposure to linear regression models.
UC Berkeley Faculty, Students and Staff are eligible for ICPSR Member pricing.
These workshops will all be held in-person at Social Science Matrix, 8th floor Social Sciences Building, UC Berkeley campus or you may attend virtually.
To register and for further information, go to https://www.icpsr.umich.edu/web/pages/sumprog/courses.html and choose the “Short Workshops” tab. Or contact Eva Seto, Associate Director Matrix via e-mail to email@example.comView Map