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Computer Science: Applied Data Science, Master's Program (Two-Year)

The course includes the following elements:

- Recommendation systems: user- and content-based recommendations, recommendation algorithms (such as neighborhood-based, collaborative filtering and matrix factorisation), context-aware recommendations, cold start, eliciting/implicit ratings, evaluation and metrics.
- Information retrieval, knowledge acquisition, knowledge representation and reasoning, the semantic web, constructing and querying knowledge graphs, extracting data from online sources and source alignment
- Probabilistic models and decision theory, decision making under uncertainty, optimisation, dynamic programming, methods for adversarial and heuristic search
- Practical methods for data mining
- Machine learning for both supervised and unsupervised learning. Algorithms for classification, prediction, and clustering.

Syllabus - here you can also find course literature

Earlier syllabuses

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).