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


  • Submit Final Report

TA e-mail : "dongykang@gmail.com"


  • Lecture Videos by the Authors: Link

  • R Tutorial (pdf)

  • Understanding R regression plot [link]


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