2019 Fall DHC5036 Machine Learning in Medicine

Course Description

    • Intermediate class for machine learning
    • Short introduction to machine learning areas
    • In-depth explanation on deep learning
    • Deep learning application to medical data
    • Flipped learning class

Prerequisite

* Though you don’t have any knowledge but want to join this class, you had better review this class before the beginning of this course. That class was the extended version of “Deep Learning for All (모두를 위한 딥러닝)” with more explanation (on both theory and Python programming) for undergraduate students.

Instructor

    • Prof. Soo-Yong Shin (sy.shin (at) skku.edu)

TA

Date & Room

    • Monday 9:00 AM ~ 11:50 AM
    • Jung Yakyong Hall, Ilwon Building

Reference

    • Stanford CS230 Deep Learning (& related Coursera courses)
      • All lecture slides can be downloaded via Syllabus & related Coursera courses
      • All lecture videos can be accessed via Syllabus (Course 1, 2 , 3 ,4, 5 in Coursera) and Lecture
      • You first watch lecture video on Coursera and then video on CS230 of each week

* You can watch the lecture video by logging-in Cousera (without payment)

Evaluation

    • Attendance: 10%
    • Quiz: 20%
      • Quiz on each class if necessary (no question or discussion at the class)
    • Assignment: 20%
      • Assignments will be given irregularly based on the topic of each class
      • Students should submit an assignment before next class
    • Project: 50%

Assignment

Project

    • Any project but related to medical domain
    • Detailed explanation document is mandatory
    • Project proposal: 10 / 50 %
    • Final demo: 40 / 50 %
    • TA will test all submitted source codes. Basic criteria:
      • No source codes: 0
      • Can run the codes without any explain: 95 (You have to prepare README.md)
      • Cannot run the codes. Based on the explain and types of errors: 0 ~ 90
      • Any question? send email to Instructor or ask question on each class

Course Schedule

(course schedule can be changed.)

    1. 9/2: Course Introduction & Machine Learning Basics
    2. 9/9: Machine Learning in Medicine & Lecture 5
    3. 9/16: Coursera Neural Networks and Deep Learning Week 1-2
      • Lecture 2 C1M1 & C1M2 in Syllabus
    4. 9/23: Coursera Neural Networks and Deep Learning Week 3-4 & Stanford CS230 Lecture 2 (on Lectures)
      • Lecture 3 C1M3 & C1M4
    5. 9/30: Coursera Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Week 1
      • Lecture 4 C2M1
    6. 10/7: Coursera Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Week 2-3
      • Lecture 4 C2M2
    7. 10/14: Coursera Structuring Machine Learning Projects Week 1-2 & Standford CS230 Lecture 3 & Lecture 6
      • Lecture 5 C3M1 & C3M2
    8. 10/21: Project Proposal
    9. 10/28: Coursera Convolutional Neural Networks Week 1-2
      • Lecture 6 C4M1 & C4M2
    10. 11/4: Coursera Convolutional Neural Networks Week 3-4
      • Lecture 7 C4M3 & C4M4
    11. 11/11: Coursera Sequence Models Week 1
      • Lecture 8 C5M1
    12. 11/18: No Class (AMIA 2019)
    13. 11/25: Coursera Sequence Models Week 2-3
      • Lecture 8 C5M2 & C5M3
    14. 12/2: Review & final questions on projects
    15. 12/9: Project Presentation
    16. 12/16: Project Presentation