2018-1 Machine Learning
Time: Fri 1:00-4:00pm
Location: Cluster Bd. R509 (학연산클러스터 509호)
Textbook: Introduction to Statistical Learning with Applications in R, James, Witten, Hastie & Tibshirani, Springer (free PDF)
Grading:
- Homework: 60%
- Project: 20%
- Attendance: 10%
- Discussion: 10%
Project
- 5.25: 정상수업 (강의실 변경: 학연산 3층 304)
- 6.1: 휴강 (비디오 강의로 대체)
- 6.8: 프로젝트 발표 (5분) : 최종발표 형식으로 준비하되, 결과 부분만 발표시점까지 결과를 발표하면 됩니다.
- Motivation
- Problem description
- Result
- 6.15: 휴강 (비디오 강의로 대체)
- 6.22: Final Report (due by 13pm, 학연산 620호, 12 페이지)
- 프로젝트 발표 내용 +
- Method 설명
- Result + discussion
- Reference
- Reproducibility (optional: github repository of experimentation scripts)
- 과제 제출 (컴퓨터로 작성하여 출력 요망)
- HW4: 4.7 Exercises #4, #6
- HW5: 9.7 Exercise #5
- 프로젝트 발표 내용 +
- Submit Final Report
TA e-mail : "dongykang@gmail.com"
Project data suggetions:
Lecture Notes
- 01. Introduction (PDF)
- 02. Statistical Learning (PDF)
- 03. Linear Regression (PDF):
- Univariate linear regression (slides ~ p13) + R introduction (code)
- HW1 (due Mar 23): 2.4 Exercises #1, #2, #4, #7, #8, #9
- 04. Linear Regression
- Multivariate linear regression (slides ~ end)
- 05. Classification (PDF, slides ~ p4)
- Lab: linear regression (code)
- HW2 (due Apr 6): 3.7 Exercises #5, #9
- 06. Classification (slides ~ p18)
- Logistic regression
- HW3 (due Apr 11): 3.7 Exercises #10, #13
- 07. Classification (slides ~ p32)
- LDA , QDA
- 08. Resampling methods (PDF)
- 09. Linear Model Selection and Regularization (PDF)
- Subset selection, Ridge regression, LASSO
- 10. Linear Model Selection and Regularization
- PCR, PLS
- R Lab (code)
- HW4 (due May 18): 4.7 Exerciese #4, #6
- 11. Moving beyond linearity (PDF)
- R Lab (code)
- HW discussion: 3.7 Exercises #10, #13 (이기찬)
- 12. Tree-based Method (video)
- 13. SVM (video1, video2)
- HW Submissions (due by June 22, 1pm, 학연산 620호)
- HW4: 4.7 Exercises #4, #6
- HW5: 9.7 Exercise #5