Assignments
Five homework assignments, each worth 12% of the course grade. Submit electronic copies via Canvas.
| # | Topic | Released | Due |
|---|---|---|---|
| 1 | Training models; Regression I | Sep 11, 11:00 AM | Sep 25, 11:59 PM |
| 2 | Regression II; Classification I | Sep 25, 11:00 AM | Oct 9, 11:59 PM |
| 3 | Classification II; Clustering I | Oct 16, 11:00 AM | Oct 30, 11:59 PM |
| 4 | Clustering II; Dimensionality reduction | Oct 30, 11:00 AM | Nov 13, 11:59 PM |
| 5 | Decision trees; Ensemble learning | Nov 13, 11:00 AM | Nov 27, 11:59 PM |
Submission and grading
- Submissions go through Canvas as a knitted PDF (and optionally the
.Rmdsource). - Solutions are graded on both correctness and clarity — use whitespace, write clear comments, and label your output.
- Partial credit is given for identifying gaps in an argument when a complete solution cannot be derived.
Late policy
Exceptions for sickness or emergency require the instructor be informed in advance.
| Days late | Credit |
|---|---|
| 0–1 | 80% |
| 1–2 | 50% |
| > 2 | 0% |
Collaboration
Discussion among students is allowed and encouraged. Each student must independently write and implement their own solution. Cite any external sources you consulted.
Quizzes
A 5-question quiz follows each of the 12 lecture topics (10% of the course grade total, all 12 averaged with your lowest two scores dropped). Released on Canvas the same day as lecture, due before the next lecture. See the syllabus for details.