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Kursusnavn (dansk):Data Mining 
Kursusnavn (engelsk):Data Mining 
Semester:Forår 2018 
Udbydes, spil (games) 
Omfang i ECTS:7,50 
Min. antal deltagere:
Forventet antal deltagere:
Maks. antal deltagere:105 
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.

Information about study structure
This course is a specialisation course on the Games study programme and SDT study programme, as well as an elective for other study programmes. 
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

The course consists of 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.

A large part of the course will be taken up by the group project, in which you can choose to work on a relevant Data Mining project of your choice. In this project you will apply the techniques and algorithms studied during the course on relevant real world problems. This will be done in groups of 3 persons.
Besides the hours planned for lectures, tutorial, and exercise, supervision sessions for the group projects are planned which complement the theory covered during the lectures and are necessary for meeting the learning objectives of the course. You will also practice presenting your work during the course in order to prepare for the oral exam. Lectures provide theoretical foundations and walk-through examples of relevant data mining algorithms while exercises focus on students discussing and implementing the central algorithms themselves. 

Obligatoriske aktivititer: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.

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:D22: Aflevering med mundtlig eksamen suppleret af aflevering., (7-scale, external exam)

The duration of this oral exam is 20 minutes

Two reports must be handed in; an individual report (2 pages) and a group report (6-8 pages).

A group should consist of 3 people.

Form of group exam: Mixed exam 2
See Study Guide -> Exams -> Course Exams -> Exam Forms for more information.  

Litteratur udover forskningsartikler:Jiawei Han, Micheline Kamber & Jian Pei: Data Mining: Concepts and
Techniques, 3rd Edition. Elsevier 2012 
Følgende personer underviser på kurset:
Navn Stilling Undervisertype Indsats (%)
Sebastian Risi Lektor(ITU) Kursusansvarlig 40
Paolo Burelli Adjunkt(ITU) Underviser 60
Carolina Bermejo Hjælpelærer(ITU) Hjælpelærer 0
Daniel Sørensen Hjælpelærer(ITU) Hjælpelærer 0
Jacob Mathias Schreiner Hjælpelærer(ITU) Hjælpelærer 0