This is an introductory course on data mining. Data Mining refers to the process of automatic discovery of patterns and knowledge from large data repositories, including databases, data warehouses, Web, document collections, and data streams. We will study the basic topics of data mining, including data pre-processing, frequent pattern mining, correlation analysis, machine learning methods for classification, prediction and clustering.

Calculus, Linear Algebra, (Some) Probability Theory

Assignments: 30%
Mid-term Exam: 25%
Final Project: 40%
Class Participation: 5%

- The grading criteria above is subject to change.

This is a stand-alone course and lecture notes with lectures will cover everything.
The textbook is recommended but not required.

Week 1. Course overview
Week 2. Data Processing
Week 3. Review of Statistics
Week 4. Similarity Measures
Week 5. Supervised vs. Unsupervised Models
Week 6. Dimension Reduction (PCA)
Week 7. Instance-based Learning
Week 8. Decision Tree
Week 9. Mid-term Exam
Week 10. Evaluation of Classifiers
Week 11. Support Vector Machine (SVM)
Week 12. Artificial Neural Network (ANN)
Week 13. Artificial Neural Network (ANN)
Week 14. Discriminative vs. Generative Models
Week 15. Text Mining
Week 16. Final Project Presentation

- The course schedule above is subject to change.

Time: Mon/Wed 2:00pm~3:15pm.
Classroom: Engineering Building II #102

TA:
Hyuna Cho (hyunacho@postech.ac.kr)
Yubin Han (yubin@postech.ac.kr)