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Grinnell College · 4 credits · UGRD

Machine learning is a branch of artificial intelligence rooted in computational statistics which focuses on the development of models and algorithms capable of identifying patterns in data. Throughout the course students will use machine learning methods to solve problems from a variety of disciplines. Topics include model validation and optimization, boosting and bagging algorithms, neural networks, transfer learning, and related topics. Students will complete a semester-long capstone project. Prerequisite: MAT-215 and STA-270.
Fall 2026 · 1 section
Aug 27 – Dec 18
| Section | Days & time | Location | Instructor |
|---|---|---|---|
| 01Class #98443-STA-370-01 | Tuesday, Thursday 1:10 PM - 2:30 PM Noyce Science Ctr 2401 (Lecture) | Noyce Science Ctr 2401 | Ryan Miller |
| 01 notes: Develop an informed critical or theoretical perspective on the social impact of data collection, including the social construction of data production, and the use of algorithmic techniques to process that data. | |||