APPLIED MACHINE LEARNING FOR BEHAVIORAL DATA SCIENCE

Prerequisites & course information

Requisites: PSYCH 610 and graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. develop and refine best practices for data wrangling, general programming, and analysis in R. Audience: Graduate 2. distinguish among a variety of learning settings: supervised learning vs. unsupervised learning, regression vs. classification Audience: Graduate 3. implement a broad toolbox of well-supported machine-learning methods: decision trees, nearest neighbor, general and generalized linear models, penalized models including ridge, lasso, and elastic-nets, neural nets, support vector machines Audience: Graduate 4. develop expertise with common feature extraction techniques for quantitative and categorical predictors Audience: Graduate 5. use natural language processing approaches to extract meaningful features from text data Audience: Graduate 6. know how to characterize how well their regression and classification models perform and they will employ appropriate methodology for evaluating their: cross validation, ROC and PR curves, hypothesis testing Audience: Graduate 7. learn to apply their skills to common learning problems in psychology and behavioral sciences more generally Audience: Graduate Learning Outcomes: 1. develop and refine best practices for data wrangling, general programming, and analysis in R. Audience: Graduate 2. distinguish among a variety of learning settings: supervised learning vs. unsupervised learning, regression vs. classification Audience: Graduate 3. implement a broad toolbox of well-supported machine-learning methods: decision trees, nearest neighbor, general and generalized linear models, penalized models including ridge, lasso, and elastic-nets, neural nets, support vector machines Audience: Graduate 4. develop expertise with common feature extraction techniques for quantitative and categorical predictors Audience: Graduate 5. use natural language processing approaches to extract meaningful features from text data Audience: Graduate 6. know how to characterize how well their regression and classification models perform and they will employ appropriate methodology for evaluating their: cross validation, ROC and PR curves, hypothesis testing Audience: Graduate 7. learn to apply their skills to common learning problems in psychology and behavioral sciences more generally Audience: Graduate