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 one-session 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 tree-based 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 |
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