CSCI 145: Data Mining
Instructor: R. Teal Witter. Please call me Teal.
Class Times: Tuesdays and Thursdays from 4:15 to 5:30pm in Kravis 164.
Office Hours: Mondays and Thursdays from 12:30 to 2:00pm in Adams 213.
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 (e.g., websites, people, LLMs).
Quizzes: There will be short quizzes at the beginning of (randomly) selected classes. These quizzes will test your understanding of the problem sets and the concepts from the prior week.
Exams: The two midterm exams are the primary method of assessing your understanding of the material.
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.
Resources: Most of the material we cover comes from either Chris Musco’s phenomenal machine learning course, or Chinmay Hegde’s fantastic deep learning course. While we do not have a textbook, we do have readings for each lecture; I highly recommend you do these readings before each class.
| Week | Tuesday | Thursday | Slides | Assignments |
| Warm Up | ||||
| Week 1 (1/20 and 1/22) | Math Review | Linear Algebra | Slides | Problem 1 |
| Week 2 (1/27 and 1/29) | PageRank | PageRank | Slides | Problem 2, Problem 3 |
| Supervised Learning | ||||
| Week 3 (2/3 and 2/5) | Linear Regression | Linear Regression | Slides | Problem 4, Problem 5 |
| Week 4 (2/10 and 2/12) | Exact Optimization | Gradient Descent | Slides | Problem 6 |
| Week 5 (2/17 and 2/19) | Polynomial Regression | Probability | Slides | Problem 7, Problem 8 |
| Week 6 (2/24 and 2/26) | Logistic Regression | Neural Networks | Slides | Problem 9 |
| Week 7 (3/3 and 3/5) | Backpropagation | Midterm Exam | Slides | |
| Week 8 (3/10 and 3/12) | Convolutional Networks | Transformers | Slides | Problem 10, Problem 11 |
| Week 9 (3/17 and 3/19) | Spring Break (No Class) | Spring Break (No Class) | ||
| Week 10 (3/24 and 3/26) | Decision Trees and Random Forests | Gradient Boosting | Slides | Problem 12, Problem 13 |
| Unsupervised Learning | ||||
| Week 11 (3/31 and 4/2) | Autoencoders | Autoencoders | Slides | Problem 14, Problem 15 |
| Week 12 (4/7 and 4/9) | Autoencoders | Autoencoders | Slides | Problem 16, Problem 17 |
| Week 13 (4/14 and 4/16) | Reinforcement Learning | Reinforcement Learning | ||
| Week 14 (4/21 and 4/23) | Project/Exam Preparation | Midterm Exam | ||
| Week 15 (4/28 and 4/30) | Diffusion | Diffusion | ||
| Week 16 (5/5 and 5/7) | Muon | Reading Day (No Class) | ||
| Week 17 (5/12 and 5/14) | Project Presentation 7-10pm | Finals (No Class) | ||