Maindonald, John, and W. John Braun (2010). “Data Analysis and Graphics Using R”. Cambridge University Press.
Fox, John, and Sanford Weisberg (2011). “An R Companion to Applied Regression”. SAGE Publications, Inc.
Machine Learning Synopsis
This workshop is an introductory-level overview of machine learning concepts for students without previous exposure to the field. We will survey some of the important elements of supervised learning, and some unsupervised learning methods are discussed. Students will work on hands-on exercises in R (Advanced knowledge of R programming is a pre-requisite).
The following topics will be discussed in varying levels of details:
Introduction to statistical learning, model selection and regularization methods (ridge and lasso)
nonlinear models, tree-based methods, random forests
support-vector machines (briefly), and clustering (k-means and hierarchical)
Requirements: Advanced knowledge of R programming and basic statistics. Please bring a laptop computer running R on any supported operating system.
Instructor bio: Bruno Abrahao is a Postdoctoral Scholar in Sociology at Stanford University. He holds a Ph.D. in Computer Science from Cornell University, and his research interests are motivated by addressing Sociological questions using large datasets.