- Dimensionality Reduction
- Graphical Models (Graphical Models)
- Variational Inference
- Bayesian learning
- Hidden Markov Models and Markov Decision Processes
- Graph Neural Networks
CM2026 Advanced Machine Learning for Data-driven-Health 7.5 credits
This course introduces advanced machine learning methods required to face the challenges of real-world health data. It has a heavy focus on supervised and self-supervised machine learning with deep neural networks. It presents strategies to deal common difficult scenarios like having small datasets or having highly-dimensional data. The course also explores probabilistic models and how they serve to deal with healthcare data.
The evaluation is done through practical individual assignments and a group project, ensuring that students can apply the learned theory to hands-on tasks.
Information per course offering
Information for Spring 2027 Start 15 Mar 2027 programme students
- Course location
KTH Flemingsberg
- Duration
- 15 Mar 2027 - 31 May 2027
- Periods
Spring 2027: P4 (7.5 hp)
- Pace of study
50%
- Application code
10270
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Places are not limited
- Target group
- No information inserted
- Planned modular schedule
- [object Object]
- Schedule
- Part of programme
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus CM2026 (Spring 2026–)Headings with content from the Course syllabus CM2026 (Spring 2026–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After passing the course, the student must be able to
· explain and justify several important methods of machine learning
· describe several types of methods and algorithms used in the field of deep learning and inference methods
· implement and apply several types of methods, models and algorithms used in the field based on a high-level description of health data
· extend and modify the methods covered in the course
Literature and preparations
Specific prerequisites
Knowledge of programming, equivalent to 6 credits
knowledge of linear algebra, corresponding to 6 credits
knowledge of statistics and probability, equivalent to 6 credits
and
basic knowledge of machine learning and artificial intelligence, corresponding to completed course CM1001 or CM2011
English 6
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
A, B, C, D, E, FX, F
Examination
- PRO1 - Group Project, 2.5 credits, grading scale: P, F
- RED1 - Assignments, 5.0 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
Examiner
Ethical approach
- All members of a group are responsible for the group's work.
- In any assessment, every student shall honestly disclose any help received and sources used.
- In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
Offered by
Main field of study
Technology and Health
Education cycle
Second cycle