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
- TensorFlow 2.X
- Python, Numpy and other necessary libraries for deep learning
- Kaggle Notebook (based on Jupyter notebook)
- Github
- https://opentutorials.org/module/3733 (git introduce)
- https://opentutorials.org/module/3746 (gui client sourcetree)
- https://opentutorials.org/module/3736 (gui client tortoisegit)
- 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.)
- 8/31: Course Introduction
- 9/7: Introduction of Machine Learning
(ASSIGNMENT #1) – (score)
- 9/14: Preliminaries
- (ASSIGNMENT #2) – (score)
- New deadline: Sep. 27
- 9/21: Linear Neural Networks (Chuseok Holiday)
- Pre-recorded Lecture at iCampus (uploaded)
- (ASSIGNMENT #3) – (score)
- 9/28: Multiplayer Perceptron (University Holiday: 공부자탄강일)
- Pre-recorded Lecture at iCampus (uploaded)
- (Assignment #4) – (score)
- 10/05: Multiplayer Perceptron: Programming Practice (TA: Mincheol Kim) (ipynb file)
- (Assignment #5) (ipynb file) – (score)
- 10/12: Convolutional Neural Network
- (Assignment #6) – (score)
10/19: CNN: Programming Practice (TA: Yoonho Choi) (ipynb file)
- (Assignment #7) (ipynb file) – (score)
10/26: Modern CNN
- (Assignment #8) – (score)
11/2: Recurrent Neural Network
- (Assignment #9) – (score)
11/9: RNN: Programming Practice (TA: Hyunwoo Choo) (ipynb file)
- (Assignment #10 (ipynb file), dataset (zip file) ) – (score)
11/16: Paper Reading & Presentation (by Students) – (score)
11/23: Paper Reading & Presentation (by Students)
- 2020710933
- 2020710919
- 2021710574
- 2021711396
- 11/30: Term Project Presentation
- 2020712045
- 2020712812
- 2021711176 (not posted due to the data privacy)
- 2021710002
- 12/7: Term Project Presentation
- 2020710933
- 2020710919
- 2021710574
- 2021711396