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Study-Unit Description
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CODE IDG5153

 
TITLE Data Mining and Game Analytics

 
LEVEL 05 - Postgraduate Modular Diploma or Degree Course

 
ECTS CREDITS 5

 
DEPARTMENT Institute of Digital Games

 
DESCRIPTION The primary goal of the study-unit is to revisit the field of game artificial intelligence (AI) and introduce non-traditional uses of AI in games. A short introduction will be given on AI areas that are currently reshaping the game AI research and development roadmap including procedural content generation, player experience modeling, and AI-based game design. The primary focus of the unit will be on game analytics and game data mining. Emphasis will be given on state-of-the-art data analytics/mining algorithms and methods for improving the gameplay experience and game development procedures.

The topics covered include:

- Revisiting game artificial intelligence. The role of game analytics;
- Basic data analysis, data preprocessing and descriptive statistics;
- Classification and prediction;
- Clustering;
- Sequence Mining;
- Advanced Data Mining Techniques;
- Data Visualization;
- Industrial game analytics - problems and needs.

Study-Unit Aims:

The aims of the unit are as follows:
- Introduction to the theory and implementation of game analytics and game data mining algorithms;
- Uses of artificial and computational intelligence for mining data in games.

Learning Outcomes:

1. Knowledge & Understanding:

By the end of the study-unit the student will be able to:
- Describe and theorize on the algorithms and domains covered in class;
- Identify tasks that can be tackled through game data mining techniques and select the appropriate technique for the problem under investigation;
- Compare the performance of different game data mining algorithms and reflect on their suitability for game AI development.

2. Skills:

By the end of the study-unit the student will be able to:
- Design and implement efficient and robust game data mining algorithms;
- Work efficiently in groups and evaluate the algorithms in data derived from commercial-standard game productions.

Main Text/s and any supplementary readings:

A. Drachen, C. Thurau, J. Togelius, G. N. Yannakakis, and C. Bauckhage, “Game Data Mining,” in Seif El-Nasr et al. (Eds.) Game Analytics — Maximizing the Value of Player Data, pp. 205-253, 2013. Springer London.

Various online articles and textbook chapters provided by the lecturer.

 
ADDITIONAL NOTES Pre-requisite Qualifications: Bachelor's in Engineering/CS or related fields; Object-oriented programming

 
STUDY-UNIT TYPE Lecture, Independent Study, Project and Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Resit Availability Weighting
Presentation (10 Minutes) Yes 10%
Oral Examination (20 Minutes) Yes 40%
Report Yes 50%

 
LECTURER/S Georgios N. Yannakakis

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.
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May 2017 - Examinations Schedule
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