EduSinglePage

The aim of the course is for the student to acquire in-depth knowledge and understanding of advanced aspects of machine learning and familiarise themselves with the current research front.

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

Kursplan - här hittar du också kurslitteratur

Tidigare kursplaner

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).