Class Information
- Lecture hours: Tue, Th 12:40pm-2:00pm
- Lecture room: Wells Hall A128
Lectures (subject to changes)
| Week | Date | Topic | Notes |
|---|---|---|---|
| Week 1 | 8/27 | 1-Introduction to Data Mining | Slides |
| 8/29 | 2-Data Preprocessing (Part 1) | Slides Supp-Python | |
| Week 2 | 9/3 | 3-Data Preprocessing (Part 2) | Slides |
| 9/5 | 4-Classification | Slides | |
| Week 3 | 9/10 | 5-Classification (SVM) | Slides ProjectProposal |
| 9/12 | 6-Classification (Bayes Networks, Bayes) | Slides | |
| Week 4 | 9/17 | 7-Classification (KNN) | Slides |
| 9/19 | 8-Classification (Neural Network) | Slides | |
| Week 5 | 9/24 | 9-Classification (Ensemble; classifier comparison) | Slides |
| 9/26 | 10-Classification (class imbalance, Multi-class) | Slides | |
| Week 6 | 10/1 | 11-Association Mining | Slides |
| 10/3 | 12-Association Mining | Slides | |
| Week 7 | 10/8 | Exam 1 | |
| 10/10 | 13-Sequence and Graph Mining | Slides | |
| Week 8 | 10/15 | 14-Regression | Slides |
| 10/17 | 15-Text Mining | Slides | |
| Week 9 | 10/22 | No class (Fall break) | |
| 10/24 | 16-Clustering | Slides | |
| Week 10 | 10/29 | 17-Clustering – Hierarchical | Slides |
| 10/31 | 18-Clustering – Density | Slides Class Coding Exercise | |
| Week 11 | 11/5 | 19-Clustering – Evaluation | Slides Exercise2-Silhouette Exercise3-Hopkins |
| 11/7 | 20-Anomaly Detection | Slides Exercise4-PCAnomaly | |
| Week 12 | 11/12 | 21-Time Series Mining + Data Visualization | Slides Codes-Data-Visualization |
| 11/14 | 22-Coding + Exam Review | Codes-Regression Codes-Graph Codes-Text | |
| Week 13 | 11/19 | Seminar Class on Generative Modeling | |
| 11/21 | Exam 2 | ||
| Week 14 | 11/26 | No class (Optional Exam) | |
| 11/28 | No class (Thanksgiving) | ||
| Week 15 | 12/3 | Project Presentation | |
| 12/5 | Project Presentation |
Homework
- Homework 1: Topic: Data Preparation.
- Homework 2: Topic: Classification.
- Homework 3: Topic: Classification and Association.
- Homework 4: Topic: Sequence Mining, Regression, Text Mining.
- Homework 5: Topic: Clustering.
Project
Project Overview: The project involves applying data mining techniques to a real-world dataset. You will work in groups to analyze the data, apply classification, clustering, or other techniques, and present your findings.
- Project Proposal: Submit a brief proposal outlining your chosen dataset and approach.
- Project Presentation: Present your findings to the class.
- Final Report: Submit a detailed report of your analysis and results.