3. 강의목표
This course introduces major data science methodologies, 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)
* Supplementary lecture videos for basic linear algebra are available in the Teaching section at https://sds.postech.ac.kr.
5. 성적평가
| 중간고사 |
기말고사 |
출석 |
과제 |
프로젝트 |
발표/토론 |
실험/실습 |
퀴즈 |
기타 |
계 |
| 20 |
20 |
10 |
20 |
30 |
|
|
|
|
100 |
| 비고 |
Assignments (20%), project (30%), exams (40%), class attitude and participation (10%)
|
7. 참고문헌 및 자료
1. Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer
2. James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Application in R. Springer.
8. 강의진도계획
The course will cover the following topics:
- Elementary statistical decision theory
- Linear methods for regression and classification
- Basis expansion and kernel methods
- Tree-based methods
- Neural networks
- Fundamental unsupervised learning methods
- Introduction to generative models
10. 학습법 소개 및 기타사항
References are freely available at
1. https://web.stanford.edu/~hastie/ElemStatLearn/
2. http://www-bcf.usc.edu/~gareth/ISL/
Also, old lecture videos (Statistical Data Mining) are available in the Teaching section at https://sds.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