2021 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
    • Dive into Deep Learning (Freely available on https://d2l.ai/)
    • Freely downloadable
    •  
    • Attendance: 10% (if online only, P/F)
    • Presentation: 30%
    • Assignment: 30%
    • Final exam: 30%
    • Final Grade

Assignment

    • All assignments should be submitted to GitHub (https://classroom.github.com/a/IxV8gufE)
    • How to submit your assignments (If you need assistant, contact Hyunwoo Choo)
    • Students should submit an assignment before next class (Monday midnight). If late, the score will be ZERO.
    • File naming convention: Assignment_1(change the number based on the assignment)_StudentID_StudentName.extension

Paper Reading

    • Candidate paper list
    • Presentation order
      • Week 1 (11/16): 2020712045, 2020712812, 2021711176, 2021710002
      • Week 2 (11/23): 2020710933, 2020710919, 2021710574, 2021711396
    • All students should choose one paper and give a presentation.
    • Each student should present the chosen paper at leas 30 minutes including Q&A. You’d better prepare the 30 min presentation. 
    • Each student should submit the presentation file (ppt or pdf) to Instructor before his/her presentation (if late, minus point)
    • Presentation order will be decided randomly on November 2. 

Course Schedule

(course schedule can be changed.)

    1. 8/31: Course Introduction
    2. 9/7: Introduction of Machine Learning
    3. 9/14: Preliminaries
    4. 9/21: Linear Neural Networks (Chuseok Holiday)
    5. 9/28: Multiplayer Perceptron (University Holiday: 공부자탄강일)
    6. 10/05: Multiplayer Perceptron:  Programming  Practice (TA: Mincheol Kim) (ipynb file)
    7. 10/12: Convolutional Neural Network
    8. 10/19: CNN: Programming Practice  (TA: Yoonho Choi) (ipynb file)

    9. 10/26: Modern CNN

    10. 11/2: Recurrent Neural Network

    11. 11/9: RNN: Programming Practice (TA: Hyunwoo Choo) (ipynb file)

    12. 11/16: Paper Reading & Presentation (by Students) – (score)

    13. 11/23: Paper Reading & Presentation (by Students)

    14. 11/30: Term Project Presentation
    15. 12/7: Term Project Presentation