Stefan Th. Gries 
Ling 105: Predictive modeling in linguistics (Spring 2022)
Syllabus and overview 
This course is a selective introduction to predictive modeling applications in linguistics. We start with a onesession intro of predictive modeling with an emphasis on regression modeling, which will survey model formulation, model selection, multifactoriality, and validation. Then, we work our way through a variety of regression modeling applications: linear regression, binary logistic regression, multinomial, and ordinal regression models. Then, one session will be concerned with model diagnostics and, perhaps, model validation. Finally, there are two sessions on treebased approaches: classification and regression trees as well as random forests. Like its prerequisite course Ling 104, this course is based on the third edition of my textbook Statistics for linguistics with R: a practical introduction (2021) and uses the open source programming language R . 





