Machine Learning: Uncovering Hidden Structure in Data (Berkeley, CA)


  • Christopher Hare, University of California at 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.

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.

Fee: Members = $1700; Non-members = $3200

Tags: machine learning

Course Sections

Section 1

Location: University of California, Berkeley -- Berkeley, CA

Date(s): July 29 - August 2

Time: 9:00 AM - 5:00 PM


  • Christopher Hare, University of California at Davis