2020 Fall DHC5036 Machine Learning in Medicine

Course Description

    • This class will introduce machine learning theory for basic and intermediate level.

    • DHC5035 Deep Learning in Medicine (Spring semester) will cover the intermediate and advanced level.

    • This class will not cover the medical applications but focus on the basic theory of machine learning.

    • Programming practice included
    • Bring your own laptop for programming practice
    •  
    • Attendance: P/F
    • Assignment: 65%
    • Final exam: 35%

Assignment

    • All assignments should be submitted to GitHub (https://classroom.github.com/a/Nx1gC00a)
    • How to submit your assignments (If you need assistant, contact Hyunwoo Choo)
    • Assignment on (almost) every class 
    • Students should submit an assignment before next class (Sunday midnight). If late, the score will be ZERO.
    • File naming convention: Assignment_1(change the number based on the assignment)_StudentID_StudentName.extension

Course Schedule

(course schedule can be changed.)

    1. 8/31: Course Introduction
    2. 9/7: Dive into Deep Learning: Introduction
    3. 9/14: Preliminaries (1)
    4. 9/21: Preliminaries (2)
    5. 10/5: Linear Neural Networks (1)
    6. 10/12: Linear Neural Networks (2): Programming Practice (by TA Hyunwoo Choo) – FYI, ipynb file, not PDF file.
      • Offline TA: Geonhee Lee
      • Online TA: Mincheol Kim, GeunHyeong Lee
      • Results
    7. 10/19: Multiplayer Perceptron (1)
    8. 10/26: Multiplayer Perceptron (2)
    9. 11/2: Multiplayer Perceptron (3): Programming Practice (by TA Mincheol Kim)
    10. 11/9: Deep Learning Computation (1): Programming Practice (by TA GeunHyeong Lee) – ipynb file

    11. 11/16: Deep Learning Computation (2): Programming Practice (by TA GeunHyeong Lee)

    12. 11/23: Convolutional Neural Networks (1)

    13. 11/30: Convolutional Neural Networks (2): Programming Practice (by  TA Geonhee Lee) – ipynb file

    14. 12/7: Mordern Convolutional Neural Networks

    15. 12/14: Final Exam