3. 강의목표
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.
4. 강의선수/수강필수사항
Calculus, Linear Algebra, (Some) Probability Theory
5. 성적평가
Assignments: 30%
Mid-term Exam: 25%
Final Project: 40%
Class Participation: 5%
- The grading criteria above is subject to change.
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
Introduction to Data Mining
|
Vipin Kumar
|
Pearson
|
2005
|
978-0133128901
|
7. 참고문헌 및 자료
This is a stand-alone course and lecture notes with lectures will cover everything.
The textbook is recommended but not required.
8. 강의진도계획
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.
9. 수업운영
Time: Mon/Wed 2:00pm~3:15pm.
Classroom: Engineering Building II #102
10. 학습법 소개 및 기타사항
TA:
Hyuna Cho (hyunacho@postech.ac.kr)
Yubin Han (yubin@postech.ac.kr)
11. 장애학생에 대한 학습지원 사항
- 수강 관련: 문자 통역(청각), 교과목 보조(발달), 노트필기(전 유형) 등
- 시험 관련: 시험시간 연장(필요시 전 유형), 시험지 확대 복사(시각) 등
- 기타 추가 요청사항 발생 시 장애학생지원센터(279-2434)로 요청