Knowledge Discovery Group

Data Mining and Machine Learning

30.10.2014: Update der Übungszeiten.

29.10.2014: Update der Vorlesungszeiten.

Persons

    Lecturer: Prof. Dr. Ansgar Scherp
    Excercises: Falk Böschen

Times

    Lectures: Wednesday, 10:00 - 11:30, CAP4 - R.1304 a (UPDATE)
    Excercises: Thursday, 11:15 - 12:00, CAP4 - R.1304 a (UPDATE)

Organization

This lecture will be taught either in German or in English (depending on the audience).

Summary

The course introduces to the topic of data mining and machine learning. It presents various different methods of data mining and machine learning, compares them, and shows their applications.

Goals

The students will be enabled to understand, reflect, and apply different methods and techniques in the areas of data mining and machine learning. The students will be able to explain the difference and commonalities of data mining and machine learning. The students will be empowered to decide which method to apply to what kind of problem.

Content

An introduction to the topic is conducted by giving an overview of the methods in data mining and machine learning. The commonalities and the differences between data mining and machine learning will be explained and discussed. Subsequently different selected methods in data mining and machine learning will be presented. In addition to the theoretical knowledge, some real-world examples of where the methods are applied will be presented.

Learning Material

Slides and other learning material can be obtained from the OpenOLAT group:
https://lms.uni-kiel.de/url/RepositoryEntry/277250198/CourseNode/89975762235584

Course Assessment

The exam will be oral or in written, depending on the size of the class. Active participation in the tutorials is prerequisite for admission to the exam.

Accounting

The module can be accounted as WInf-DMML (6 ECTS) in the studies of MSc Business Informatics and MSc Computer Science.

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