DATA 401 Applied Machine Learning
Statistical machine learning (often referred to simply as statistical learning) has arisen as a recent subfield of statistics. It emphasizes the interpretability, precision, and uncertainty of machine learning models. This course assesses the accuracy of several supervised and unsupervised machine learning models for both regression and classification. Topics include the bias-variance trade-off, training and test datasets, resampling methods, shrinkage and dimension reduction methods, non-linear modeling techniques such as regression splines and generalized additive models, and decision tree-based methods. Applications include examples from medicine, biology, marketing, finance, insurance, and sports.
Note: Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at MATH@metrostate.edu.
Prerequisites
Special information
4 Undergraduate credits
Effective August 15, 2022 to present
Learning outcomes
General
- Assess the accuracy of both supervised and unsupervised machine learning models.
- Compare the tradeoffs between flexibility and interpretability of several machine learning methods
- Compare the competing properties of bias and variance of statistical learning methods
- Apply the appropriate statistical learning model for analyzing a dataset using statistical software
- Create the computer output for a statistical learning analysis
- Interpret the results and conclusions for statistical learning techniques applied to actual data in a variety of disciplines
Spring 2025
| Section | Title | Instructor | books | eservices |
|---|---|---|---|---|
| 50 | Applied Machine Learning | Jacobson, David Willia | Books for DATA-401-50 Spring 2025 | Course details for DATA-401-50 Spring 2025 |