Sie sind hier

Machine Learning and Data Mining

Welcome to the website of Machine Learning and Data Mining course.

On this page, we want to provide you with the most important information and course material for the course. Here, we publish slides for lectures and tutorials, exercises and home assignments. You can simply sync this Github repository for always being up-to-date and having access to all course materials!

Lectures

Lectures take place every week on Wednesdays, 10 a.m. in room H 010. The following agenda is a first layout and may change during the course.

Tutorials

Tutorials take place about every two weeks on Friday, 12.00h in room M 001. The exact dates are:

Assignments

Home assignments are mandatory and to be solved in groups of 2-3 participants. You must complete at least 5 of the 6 home assignments to be admitted to the final exam.

  • 1. Home Assignment: Solution
    Submit at latest by 10th of November
  • 2. Home Assignment: Solution
    Submit at latest by 25th of November
  • 3. Home Assignment: Task Solution
    Submit at latest by 16th of December
  • 4. Home Assignment: Task Reddit test Reddit train
    Submit at latest by 14th of January
  • 5. Home Assignment: Task
    Submit at latest by 27th of January
  • 6. Home Assignment: Task Solution
    Submit at latest by 15th of February

Exam

Recommended Literature

  • T. Mitchell: "Machine Learning", 1997
  • J. Han, M. Kamber, J. Pei: "Data Mining: Concepts and Techniques", 2011
  • I. Witten, E. Frank, M. Hall: "Data Mining: Practical Machine Learning Tools and Techniques", 2011
  • C. Bishop: "Pattern Recognition and Machine Learning", 2008
  • M. Ester, J. Sander: "Knowledge Discovery in Databases: Techniken und Anwendungen", 2013 (german language)

More literature can be found in the lecture slides

Team

The course will be coached by the following persons:

For inquiries please consult the newsgroup! (.infko.mldm)

Beteiligte: 

Prof. Dr. Markus Strohmaier

strohmaier@uni-koblenz.de

Dr. Florian Lemmerich

florian.lemmerich@gesis.org

Dr. Philipp Singer

philipp.singer@gesis.org

Dr. Christoph Kling

datascience@c-kling.de