Syllabus
A graduate introduction to machine learning through educational applications.
Syllabus in progress as of .
Learning goals
- Define a prediction task and identify information available at the intended prediction time.
- Build a reproducible workflow with preprocessing, validation, tuning, and held-out evaluation.
- Compare baseline and trained models using metrics appropriate to the outcome.
- Examine uncertainty and performance across student groups.
- Explain the distinction between prediction and evidence about an intervention.
- Communicate an educational analysis through reproducible code and a research paper.
Software
Students are free to use either R or Python for assignments and the course project. The same questions and grading criteria apply to both languages.
Course work
Course work includes applied problem sets, in-class modeling exercises, and a semester project with progress check-ins.
Readings
An Introduction to Statistical Learning offers R and Python editions. Tidy Modeling with R provides additional R workflow examples.
Generative AI use
Generative AI is permitted for concept explanations, brainstorming, coding assistance, debugging, and editing, unless an assignment specifies different rules. AI use is optional. No paid tool is required.
You must understand and explain your submitted code, justify your modeling choices, and interpret results in your own words. Test code, verify calculations, and check citations against original sources. You are responsible for the accuracy of the entire submission, including AI contributions.
Include a short AI-use statement in each submission naming the tool or model, the purpose, the affected code or text, and how you checked the output. If you used no generative AI, state this. Retain relevant prompts and outputs for discussion if requested.
Do not enter identifiable student data, grades, confidential materials, or restricted datasets into external AI tools. Follow university data-handling requirements. Do not fabricate results or citations, conceal AI contributions, or use AI for work explicitly designated for independent completion.
Unacknowledged or unauthorized AI use falls under Vanderbilt’s academic integrity procedures. Ask the instructor before using AI when an assignment’s rules are unclear.
University guidance: Academic Integrity and Generative AI.
Course communication
Enrolled students will use Brightspace for announcements, submissions, grades, and feedback. This website provides selected public learning materials.
Instructor
Conrad Borchers, Vanderbilt University. [email protected].