Kurs, master’s level
15 hp
Malmö | daytime | 100%
30 mars 2026 - 7 juni 2026
Kod för kursen: DA633E

Om kursen

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.

Denna kursen ges som del av program:

Kursinnehåll

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

Behörighetskrav

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).

Kurslitteratur

Kursvärdering

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).

Kontakt