The course covers a number of machine learning methods and concepts, including state-of-the-art deep learning methods, with example applications in the medical imaging and computational biology domains.
This GitHub page contains all the general information about the course and the study materials. The Canvas page of the course will be used only for sharing of course information that cannot be made public (e.g. Microsoft Teams links), submission of the practical work and posting questions to the instructors and teaching assistants (in the Discussion section).
The 2020 edition of the course will be given in a hybrid manner combining on-campus and on-line education. We have a limited capacity for around 30-35 students to attend the lectures on-campus. The lectures will also be streamed on-line via Microsoft Teams for the students that want to attend remotely.
Because the room capacity is less than the number of students that registered for the course, you will have to subscribe to attend a lecture on-campus. The details about how to do this will be posted on Canvas.
The schedule is as follows:
- Lectures, time: Mondays 13.30 - 15.30, location: Atlas - 1.210 and on-line via Microsoft Teams (the link will be shared on Canvas)
- Practical work, time: Mondays 15.30 - 17.30, location: on-line via Microsoft Teams (the link will be shared on Canvas)
Since the practical sessions are immediately after the lectures, if you attend the lectures on-line you might not have sufficient time to travel home for the practical session. The University is working on ensuring that there are sufficient number of safe workspaces on-campus so that you can log in from there.
The practical work, a.k.a. guided self-study (begeleide zelfstudie), will be done in groups. The groups will be formed in Canvas and you will also submit all your work there (check the Assignments section for the deadlines). Your are expected to do this work independently with the help of the teaching assistants. Each group will be assigned a teaching assistant that you can contact via Microsoft Teams during the practical sessions (the details and links will be posted in Canvas). You can also post your questions in the Discussion section in Canvas at any time (i.e. not just during the practical sessions).
The lectures are mainly based on the selected chapters from following two books that are freely available online:
- Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman
Additional reading materials such as journal articles are listed within the lecture slides.
The practical assignments for this course will be done in Python. It is essential that you correctly set up the Python working environment before the start of the course so there are no delays in the work on the practicals.
Please carefully follow the instructions available here on setting up the working environment and (optionally) a Git workflow.
IMPORTANT: All materials tagged with (tentative) are not updated from the previous edition of the course and might change for this edition. However, any changes made will not be substantial and you can still use the materials to get an early peek at the content.
- (tentative) Introduction slides
- (coming soon) Instructions
- (tentative) Lecture slides
- (tentative) Practical work
- (tentative) Lecture slides
- (tentative) Practical work
- (tentative) Lecture slides
- (tentative) Practical work
- (tentative) Lecture slides
- (tentative) Practical work
- (tentative) Lecture slides part 1 and part 2
- (tentative) Practical work @ Google Colab
- (tentative) Lecture slides
- (tentative) Practical work
- (coming soon) Lecture slides
- (coming soon) Practical work
After completing the course, the student will be able to:
- Recognise how machine learning methods can be used to solve problems in medical imaging and computational biology.
- Comprehend the basic principles of machine learning.
- Implement and use machine learning methods.
- Design experimental setups for training and evaluation of machine learning models.
- Analyze and critically evaluate the results of experiments with machine learning models.
The assessment will be performed in the following way:
- Work on the practical assignments: 25% of the final grade (each assignment has equal contribution);
- Reading assignment: 10% of the final grade;
- Final exam: 65% of the final grade.
Intermediate feedback will be provided as grades to the submitted assignments.
The grading of the assignments will be done per groups, however, it is possible that individual students get separate grade from the rest of the group (e.g. if they did not sufficiently participate in the work of the group).
The students will receive instruction in the following ways:
- Lectures;
- Guided practical sessions;
- Contact hours with the project instructors for questions, assistance and advice;
- Online discussion (in Canvas, see below).
Course instructors:
- Mitko Veta
- Federica Eduati
Teaching assistants:
- Suzanne Wetstein
- Oscar Lapuente Santana
- Yasmina Al Khalil
8DB00 Image acquisition and Processing, and 8DC00 Medical Image Analysis.
The [course page in Canvas] will be used for submission of the assignments, scheduling of the lectures and contact hours and announcements. The students are highly encouraged to use the Discussion section in Canvas. All general questions (e.g. issues with setting up the programming environment, error messages etc., general methodology questions) should be posted in the Discussion section in Canvas and not asked during the contact hours.
This page is carefully filled with all necessary information about the course. When unexpected differences occur between this page and Osiris, the information provided in Osiris is leading.