Syllabus for Data Mining I

Informationsutvinning I

A revised version of the syllabus is available.

Syllabus

  • 5 credits
  • Course code: 1DL360
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N, Technology A1N
  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2010-03-18
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2010
  • Entry requirements:

    120 credits with mathematics 30 credits including mathematical statistics, computer science and/or technology 45 credits including an advanced course in programming and database design I. Algorithms and Data Structures I is recommended.

  • Responsible department: Department of Information Technology

Learning outcomes

To pass, the student should be able to:

  • explain different methods to extract processed information from large amounts of data, both in theory and in practical application
  • use these methods with appropriate tools and
  • evaluate and compare the suitability of different methods.

Content

Introduction to data mining, its terminology and overview over various types of data and its properties, an overview of different methods to explore and visualise large amounts of data, introduction to classification methods, introduction to clustering methods, introduction to association analysis, handling of personal integrity in the area of data mining.

The subjects are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical amounts of data.

Instruction

Lectures, seminars, laboratory sessions and written assignments. Guest lecture.

Assessment

Written examination (3 HE credits) and assignments that are presented orally and/or written (2 HE credits).

Reading list

Reading list

Applies from: Autumn 2010

Some titles may be available electronically through the University library.

  • Tan, Pang-Ning; Steinbach, Michael; Kumar, Vipir Introduction to Data Mining

    1st or international edition: Addison-Wesley, 2006

    Find in the library

Last modified: 2022-04-26