| Kursusnavn (dansk): | Data Mining |
| Kursusnavn (engelsk): | Data Mining |
| Semester: | Forår 2015 |
| Udbydes under: | cand.it., spil (games) |
| Omfang i ECTS: | 7,50 |
| Kursussprog: | Engelsk |
| Kursushjemmeside: | https://learnit.itu.dk |
| Min. antal deltagere: | 12 |
| Forventet antal deltagere: | 30 |
| Maks. antal deltagere: | 96 |
| Formelle forudsætninger: | Students must have experience with and be comfortable with programming, and be capable of independently implementing algorithms from descriptions in pseudocode. 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 assignments.
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Information about study structure
This course is a specialisation course on the Games study programme, as well as an elective for other study programmes such as SDT. |
| Læringsmål: | After the course the students 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. |
| Fagligt indhold: | This course gives an introduction to the field of data mining. The course is relatively practically oriented, focusing on applicable algorithms. Practical exercises will involve both use of a freely available data mining package and individual implementation of algorithms.
The course will cover the following main topics:
- The data mining process
- Data pre-processing
- Pattern and association mining
- Classification and prediction
- Cluster analysis
Additionally the course will touch on topics including data warehousing, validation and recommender systems. Application examples will be given from domains including e-commerce, computer games and finance. |
| Læringsaktiviteter: | 12 forelæsninger + frivillige øvelsestimer 7 weeks of lectures, followed by 7 weeks of supervised group projects. Most lectures are followed by a lab exercise, which involves independent programming. Students must be able to program. The default language is Java, but other languages are possible.
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See the schedule here:
link to the time table
The schedule will be available shortly before the beginning of the term. |
| Obligatoriske aktivititer: | Content
Workload
Activities
There will be one mandatory assignment, consisting of using self-implemented data mining techniques on a simple data set and writing a report about it.
Feedback
What if the student fails to pass a mandatory activity:
Be aware: The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt. |
| Eksamensform og -beskrivelse: | X. experimental examination form (7-scale; external exam) The duration of this oral exam is 20 minutes.
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| Litteratur udover forskningsartikler: | Jiawei Han, Micheline Kamber & Jian Pei: Data Mining: Concepts and
Techniques, 3rd Edition. Elsevier 2012 |
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Kursusoversigt