Course syllabus

Lp2 HT25 (7.5 hp)
The course is offered by the Department of Computer Science and Engineering

 

Welcome to DAT635: Machine learning in healthcare!

Healthcare is facing tremendous challenges with an ageing population, a lack of medical staff and increased cost. At the same time, machine learning & AI are developing rapidly and are well suited to tackle some of the repetitive tasks of general and specialized care. The aim for this course is to give knowledge and understanding of learning problems in the healthcare domain and machine learning methods to solve them. It should also provide experience with applying these tools in practical problem solving on real-world health data.

Note: This is the second run of this course. Please help us improve it by being patient and leaving constructive feedback if you have it. 

 

Teaching team

Examiner:

Fredrik Johansson (Chalmers, CSE) fredrik.johansson@chalmers.se

Teaching Assistants: 

Herman Bergström (Chalmers, CSE) hermanb@chalmers.se

Alireza Bordbar (Chalmers, E2) bordbar@chalmers.se

For contacting the course staff, please prioritize direct emails over Canvas-based communication.
We are rarely notified of Canvas messages and are therefore prone to late responses to these. 

Guest lecturers

Sam Polesie (GU/SU)

Patrick Royer (GU/SU)

Lukas Hilgendorf (GU/SU)

Announcements

• December 18. The grades for Assignment 2 have now been posted. 
• December 11. Assignment 3 has now been posted!
• December 8. The lecture on causal graphs will not be included in this year's course and its contents will not be on the exam. 
• December 3. The lecture on December 10th will be pre-recorded and delivered as a video as there is a clash between the event CHAIR: Students of AI and the lecture. 
• December 2. The grades and feedback for Assignment 1 have now been posted. 
• December 1: Today, we will cover a few problems from the 2024 exam and the 2025 August exam.
• November 26: The order of modules 6-7 has been swapped to accomodate the guest lectures and give more time for Assignment 3. 
• November 25: Assignment 2 has now been posted!
• November 17: Corrected time for consultation: Friday 21st of November: 10-12!
• November 3: Practice exams posted
• October 27: Check the short introduction quiz 

Learning objectives

The learning objectives are specified in the syllabus on Studieportalen.

Schedule

Overview:	Lectures	Office hours	Notes:
TimeEdit	Mondays 10:00–11:45 Wednesdays 13:15-15:00	Before the deadline of each assignment: November 21, 10:00–11:45 December 4 , 10:00–11:45 December 19 , 10:00–11:45 Room: EDIT 5128 Come to the office hours prepared with specific questions for the teaching assistants

Course literature

• The course uses no textbook but slides, papers, and lecture notes. 
• Each module has a page with resources for the corresponding topic (see below)
• Lecture notes: 
  • DAT635__Lecture_Notes.pdf

Course contents

• The course is structured as 8 different modules corresponding to different aspects of machine learning in healthcare. 
• Each module is taught through 2 lectures

1. Introduction

2. Patient-level learning & prediction

3. Medical text & administrative data

4. Epidemiology & population-level learning I

5. Epidemiology & population-level learning II

6. Uncertainty & missing values

7. Machine learning in clinical workflow

8. Toward integration (remote)

 

Assignments & exam

• The course contains a total of 3 graded take-home assignments (see criteria below) submitted individually through Canvas.
  • The hand-in assignments will test your ability to implement machine-learning solutions to healthcare problems.
  • This will involve analyzing provided medical data sets in Python.
  • To complete the assignments, you are expected to have access to a computer. These will not require access to a GPU. 
• At the end of the course, in January, there will be a written exam.
  • The exam will test your knowledge of the concepts taught in the course, as well as your mathematical ability associated with solving them.
  • No aids will be permitted during the exam
• Practice exams: Exams

Grading

• Each completed assignment will be awarded a score in the range 0–10 points—the maximum total number of assignment points is 30. The written exam is also awarded between 0–10 points. 
• To pass the course (grade 3), the student must obtain at least 40 % of the total points in each of the hand-in problems and at least 40 % of the total points in the exam assignment. Higher grades require, in addition to the above, that the combined score from the hand-in problems and the exam, weighted by 60 % and 40 % respectively, exceeds 60 % for grade 4 or 80 % for grade 5.

• Example: Student A receives a total of 21 points on the assignments (70%) and 5 points on the exam (50%). The combined score is 0.6*70% + 0.4*50% = 62%. The student will receive the grade "4". 
• Example: Student B receives a total of 21 points on the assignments (70%) and 4 points on the exam (40%).  The combined score is 0.6*70% + 0.4*40% = 58%. The student will receive the grade "3". 
• Example: Student C receives a total of 24 points on the assignments (80%) and 3 points on the exam (30%).  The exam score is below 40%. The student will receive the grade "U". 

Failed assignments, resubmissions and late submissions

• Failed assignments can be replaced by a single resubmission during each run of the course. Twice failed assignments can be completed in the next exam period. 
• Resubmissions will be awarded a reduced number of points. The first 4 points (pass) are awarded as-is, any points beyond 4 are counted as half. For example: 8p will be counted as 4+(8-4)/2 = 6p. 
• Only failed assignments can be resubmitted. 
• Late assignments will be counted as resubmissions: I.e., there can only be one (no resubmission after the first), and the points are reduced in the same way. 
• Blank assignment submissions are counted as no assignment being submitted. 

A note on cheating and use of AI tools

• Handing in solutions to assignments copied verbatim or near-verbatim (e.g., only changing notation or variable names) from another student or from resources on the web is considered cheating. 
• You are allowed to use AI tools, such as Chat-GPT or Copilot, to assist you in your implementation tasks provided that you disclose the nature of the use in your hand-in.