Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST

Machine Learning and Data Mining

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Wintersemester 2020 / 2021

This course will be held online. More information about video recording and live-streams will follow soon.

Exam admissions from the Machine Learning and Data Mining course of the winter term 2019/2020 are still valid, regardless whether one participated in an exam. Thus, students who attended the course one year ago and scored at least 60% of the points in the assignments must not register for an assignment group again.

The course “Machine Learning and Data Mining (MLDM)” covers the fundamentals and basics of machine learning and data mining. The course provides an overview of a variety of MLDM topics and related areas such as clustering and classification.

Excited to participate? Then, you need to:

  1. Register to the lecture in Klips. Mandatory for all students who want to participate.
  2. Add yourself into a group for working on mandatory assignments here until 11th November. Applies only for those students who take the course the first time or did not gather the exam admission in the last winter term.

For inter-student communication, please use the newsgroup infko-mldm.

Important Information

To whom?

Master and Bachelor students in:

  • Web Science
  • Computer Science
  • Computational Visualistics
  • Business Informatics


Course Material

Slides, lecture recordings and additional material will be provided along with the progress of the lecture. Below, the table of dates and topics is subject to changes.

Date Topic Lecturer Slides Video Lecture
4th November Introduction Zeyd Boukhers
11th November Data Preprocessing Raphael Menges
18th November Data Transformation Raphael Menges
25th November Clustering 1 Raphael Menges
2nd December Clustering 2 Raphael Menges
9th December KNN and Naive Bayes Tjitze Rienstra
16th December Decision Tree Tjitze Rienstra
6th January Linear Classification Zeyd Boukhers
13th January Neural Network Zeyd Boukhers
20th January Deep Learning Zeyd Boukhers
27th January Hidden Markov Chain Tjitze Rienstra
3rd February
10th February


Tutorials will discuss the solutions to the last assignment and discuss the exercises of the current one. Solutions will not be uploaded!

Date Topic Lecturer Materials
12th November Organizational Notes and Python Introduction Raphael Menges
19th November Machine Learning Fundamentals Raphael Menges
26th November Data Preprocessing Raphael Menges
3rd December Data Transformation Raphael Menges
10th December Clustering Raphael Menges
17th December KNN Tjitze Rienstra
7th January Naive Bayes Tjitze Rienstra
14th January Decision Tree Tjitze Rienstra
21st January SVM Zeyd Boukhers
28th January Neural Network Zeyd Boukhers
4th February Deep Learning Zeyd Boukhers
11th February Hidden Markov Chain Tjitze Rienstra


Assignments will be provided along with the progress of the lectures.

Please form groups of 3 to 4 people to work on the assignments here, until 11th of November! The assignments are graded before the next tutorial and it is mandatory to reach 60% of the points in total over all assignments to be allowed to participate in the exam. E.g., if there are 10 assignments each 100 points, you need in total at minimum 600 points in sum over all assignments to participate in the exam.

Release Date Deadline (9:00 AM!) Topic Sheets Remarks
9th November 16th November Machine Learning Fundamentals
16th November 23rd November Data Preprocessing
23rd November 30th November Data Transformation
30th November 7th December Clustering
7th December 14th December KNN
14th December 21st December Naive Bayes
4th January 11th January Decision Tree
11th January 18th January SVM
18th January 25th January Neural Network
25th January 1st February Deep Learning
1st February 8th February Hidden Markov Chain


  • Wissenschaftlicher Mitarbeiter
  • B 104
  • +49 261 287-2765
  • Wissenschaftlicher Mitarbeiter
  • B 112
  • +49 261 287-2779
  • Wissenschaftlicher Mitarbeiter
  • B 104
  • +49 261 287-2862