Data Science in Games
AbstractThis course gives an introduction to the field of data science with applications to game development. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining packages and individual implementation of algorithms.
DescriptionThe course will cover the following main topics:
- The data mining process
- Cluster analysis
- Data pre-processing
- Pattern and association mining
- Classification and prediction
Application examples will be given from domains including demographics, image processing and games.
Students must have experience with and be comfortable with programming, and be capable of independently implementing algorithms from descriptions. This corresponds to at least having passed an introductory programming course, and preferably also an intermediate-level programming course. The course will contain compulsory programming.
Intended learning outcomes
After the course, the student should be able to:
- Analyze data mining problems and reason about the most appropriate methods to apply to a given dataset and knowledge extraction need.
- Implement basic pre-processing, association mining, classification and clustering algorithms.
- Apply and reflect on advanced pre-processing, association mining, classification and clustering algorithms.
- Work efficiently in groups and evaluate the algorithms on real-world problems.
- Describe different technologies for big data storage and processing
Ordinary examExam type:
D: Submission of written work with following oral, external (7-trinsskala)
D2G: Submission of written work for groups with following oral exam supplemented by the work submitted.