DATA 4350: Practical Machine Learning

This 3-hour course provides a practical introduction to machine learning with minimal mathematical theory. Students will learn to select appropriate models for different problems, build and evaluate models using modern statistical software, and effectively communicate results to non-technical audiences. The course emphasizes hands-on application and interpretation of machine learning.

Course Goals

By the end of this course, students will:

1. Understand when and why to use machine learning in real-world contexts
2. Appreciate the tradeoffs between model complexity, interpretability, and performance
3. Develop intuition for matching modeling approaches to business problems
4. Recognize the importance of proper validation and the dangers of overfitting
5. Gain confidence in their ability to conduct independent data analysis projects

 Prerequisites

DATA 3520 (Data Analysis) or equivalent with instructor approval

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Search for "DATA 4350" in the Schedule of Classes to register. For more help with registration, please review the resources provided by the Registrar's Office.

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Data is for everyone. Contact the Goldman Center for Student Accessibility for assistance. We can't wait to see you in class.