About the course

The purpose of the course is for the student to acquire in-depth knowledge and understanding of advanced aspects of machine learning and familiarise him or herself with recent developments in this field.

Course content

The course contains the following elements:

- Data transformation, Data Augmentation, adjustment/calibration of model parameters (including Advanced Feature Extraction, Hyper-parameter Optimisation)
- Interactive machine learning methods (including Human-Machine Collaboration, Active Learning, Online learning, Incremental Learning, Learning from Data Streams)
- Meta-learning algorithms and Ensemble Methods
- Advanced algorithms for supervised learning and unsupervised learning (with emphasis on discriminative and generative Deep Learning architectures)
- Reinforcement learning (including Policy Search, Policy Iteration, Value Iteration, Q-learning)
- Trends and current front line research in machine learning

Syllabus and course literature

You can find a list of literature and other details about the course in the syllabus.

Entry requirements and selection

Entry requirements

1. Bachelor of Science (at least 180 higher education credits) in computer science or related subjects such as mathematics, informatics, telecommunications, electrical engineering, physics.

2. Knowledge equivalent to English 6 at Swedish upper secondary level.

3. At least 15 credits in programming.

4. At least 7.5 credits in mathematics.

5. Passing grade in the course Statistical methods for Data Science (MA660E)

Participation in the course also requires knowledge obtained from the course Artificial intelligence for Data Science (DA631E).


100% University credits completed

Course literature

Course evaluation

The University provides students who are taking or have completed a course with the opportunity to share their experiences of and opinions about the course in the form of a course evaluation that is arranged by the University. The University compiles the course evaluations and notifies the results and any decisions regarding actions brought about by the course evaluations. The results shall be kept available for the students. (HF 1:14).