MATH 166: Data Mining
Instructor: R. Teal Witter. Please call me Teal.
Class Times: We meet Tuesdays and Thursdays; Sec. 1 is scheduled from 2:45 to 4:00pm, and Sec. 2 from 4:15 to 5:30pm.
Office Hours: Before I schedule office hours, please fill out this when2meet so we can find times that work for all of us.
Participation: I expect you to engage in class, ask questions, and make connections. To receive credit, please fill out this form after every lecture.
Quizzes: There will be short quizzes at the beginning of our Tuesday classes. These quizzes will test your understanding of the problem sets and the concepts from the prior week.
Problem Sets: Your primary opportunity to learn the material will be on problem sets. You may work with others to solve the problems, but you must write your solutions by yourself, and explicitly acknowledge any outside help (websites, people, LLMs).
Exams: The two midterm exams are designed to give you a multiple ways of demonstrating your understanding. The first is a written midterm focused on supervised learning. The second is a verbal exam, à la a technical interview.
Project: The project offers a chance to explore an area that interests you, practice writing high quality code, and develop your ability to communicate technical ideas to an audience. In addition to your codebase, you will write a report and give a short presentation at the end of the semester.| Week | Tuesday | Thursday | Slides | Assignments |
| Warmup | ||||
| Week 1 (8/27 and 8/29) | Linear Algebra | PageRank | ||
| Supervised Learning | ||||
| Week 2 (9/2 and 9/4) | Linear Regression | Optimization | ||
| Week 3 (9/9 and 9/11) | Gradient Descent | Polynomial Regression | ||
| Week 4 (9/16 and 9/18) | Probability | Logistic Regression | ||
| Week 5 (9/23 and 9/25) | Support Vector Machines | Constrained Optimization | ||
| Week 6 (9/30 and 10/2) | Kernel Methods | Neural Networks | ||
| Week 7 (10/7 and 10/9) | Decision Trees | Gradient Boosting | ||
| Week 8 (10/14 and 10/16) | Fall Break (No Class) | Autoencoders | ||
| Beyond Supervised Learning | ||||
| Week 9 (10/21 and 10/23) | Midterm Exam | Variational Autoencoders | ||
| Week 10 (10/28 and 10/30) | Principal Component Analysis | Semantic Embeddings | ||
| Week 11 (11/4 and 11/6) | Reinforcement Learning | Reinforcement Learning | ||
| Week 12 (11/11 and 11/13) | PAC Learning | PAC Learning | ||
| Week 13 (11/18 and 11/20) | Active Learning | Interpretability | ||
| Week 14 (11/25 and 11/27) | Final Exam | Thanksgiving (No Class) | ||
| Week 15 (12/2 and 12/4) | Project Preparation | Project Preparation (No Class) | ||
| Week 16 (12/9 and 12/11) | Sec. 2 Presents 7–10pm | Sec. 1 Presents 2–5pm | ||