CM2026 Advanced Machine Learning for Data-driven-Health 7.5 credits

-      Dimensionality Reduction

-      Graphical Models (Graphical Models)

-      Variational Inference

-      Bayesian learning

-      Hidden Markov Models and Markov Decision Processes

-      Graph Neural Networks

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

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

• 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.

• 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.