3. 강의목표
This course will cover theories and applications of basic (statistical) data mining techniques including regression, classification and unsupervised learning.
4. 강의선수/수강필수사항
- Probability and Statistics for Engineers (IMEN 272) or Probability and Statistics (MATH 230)
- Applied Linear Algebra (MATH 203)
- R programming for data analytics (IMEN 491, online course): Highly recommended to take simultaneously!
* Important supplements for this class in linear algebra, probability and statistics.
http://sds.postech.ac.kr/statistical-data-mining-imen-472/
Students should study these supplements before joining first class to follow up class progress.
5. 성적평가
Assignments (50%) Final project (40%) Attitude (10%)
6. 강의교재
| 도서명 |
저자명 |
출판사 |
출판년도 |
ISBN |
|
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
|
Hastie, T., Tibshirani, R. and Friedman, J.
|
Springer
|
2009
|
|
7. 참고문헌 및 자료
1. James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Application in R. Springer.
2. 전치혁. (2012). 데이터마이닝 기법과 응용, 한나래아카데미
9. 수업운영
First class will be held through Zoom.
Every student who wants to participate in our class should install a zoom and create a free account before participating in the class. After that, enter the meeting ID and password below to participate in the class.
*zoom meeting ID : 772-041-1879
*password : 123456
For more information about installing a zoom and creating a free account, please refer to the 'Information Guide - for ZOOM students' post in Online Course Support(Q/A)
10. 학습법 소개 및 기타사항
Textbooks are freely available at
1. https://web.stanford.edu/~hastie/ElemStatLearn/
2. http://www-bcf.usc.edu/~gareth/ISL/
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