Institute for Web Science and Technologies · Universität Koblenz - Landau
Institute WeST
This course is from a past or future semester. If you are looking for current courses, go to the course overview.

Machine Learning and Data Mining

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Winter Term 2019 / 2020

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 optimization and deep learning. Excited to participate? Then, you need to:

  • Register to the lecture in Klips
  • Register to one tutorial in Klips
  • Add yourself into a group for working on mandatory assignments here until 25th October

When you are newly enrolled and have not yet access to the infrastructure of our University, you may still take part in the course. You must perform the following steps until 25th of October:

  1. Come up with groups of four students. If your group has less than four students, we will merge you with other students without further notice.
  2. Send one (!) email per group (!) to with the title “MLDM GROUP REGISTRATION” and the full names (as provided at enrollment) + current email address of each team member.
  3. We will tell you your group name for submission and the further process of submission then.

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



First Exam:

  • When: February 12 at 08:30 (Arrive no later than 15 minutes before the start time!)
  • Where: M 001
  • Duration: 90 minutes
  • Registration (Klips): Open from December 30 to February 5 (Do Not miss the deadline!)
  • Cancellation (Klips): Until February 7
  • If you are registered, find your seat here

Second Exam:

  • When: June 22 at 08:30 (Arrive no later than 15 minutes before the start time!)
  • Where: M001 and E011
  • Duration: 90 minutes
  • Registration (Klips): Open from March 01 to June 5 (Do Not miss the deadline!)
  • Cancellation (Klips): Until June 22 (the day of the exam)

Very important:

  • the date and place are subject to change. Therefore, please register for the exam so that you receive related emails. Please do not send emails asking about the exam, because you will receive everything via email if you are registered. Otherwise, please visit the web page regularly.
  • This exam is an optional offer for you. The next exam will take place at the end of the WiSe20/21 and you can also participate directly in this exam. Acquired assignment score from WiSe19/20 will be accepted for the next exam. So if you feel uncomfortable writing an exam under the current conditions, please wait until the winter semester.
  • The cancellation deadline is the day of the exam. So, if you notice shortly before the exam that you have symptoms of illness or that the situation is simply too stressful for you, you can still cancel without any consequences.
  • Please bring a face mask with you as you might be required to wear it.
  • Please note that we cannot offer any review for this exam.
  • Please see Exams under the Covid 19 restrictions

Important note: You must have gathered at least 60% of the points across the assignments of this semester or have failed the exam in the last year to be allowed to participate in the exam. Admissions from recent years are not valid.

Further note: We allow scientific calculators, but no programmable or graphical calculators in the exam.

Organizational Information

Lecture (Klips)

  • Lecturer: Dr. Zeyd Boukhers
  • Wednesdays, 08:00 - 10:00 in M 001
  • Consultation: Mondays at 09:00 in B114 or by appointment (

Tutorial (Klips)

  • Instructors: Akram Sadat Hosseini, Raphael Menges and Qusai Ramadan
  • Thursdays, 14:00 - 16:00, M 001
  • Thursdays, 16:00 - 18:00, M 001
  • You don't need to come to both slots for the tutorial. We will cover the same material on both.

Please form groups of four people to work on the assignments here, until 25th of October! 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 10 points, you need in total at minimum 60 points in sum over all assignments to participate in the exam.

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
23rd Oct Introduction Zeyd Boukhers PDF (Uni- account) or PDF (With passwd) Video
30th Oct Data Preprocessing Zeyd Boukhers PDF (Uni- account) or PDF (With passwd) Video
6th Nov Classification Part 1 Zeyd Boukhers PDF (Uni- account) or PDF (With passwd) Video
13th Nov Classification Part 2 Zeyd Boukhers PDF Video
20th Nov Decision Tree Zeyd Boukhers PDF Video
27th Nov Random Forest Zeyd Boukhers PDF Video
4th Dec Linear Classification Zeyd Boukhers PDF Video
11th Dec Neural Networks Zeyd Boukhers PDF Video
18th Dec Test Exam
8th Jan Deep Neural Networks Zeyd Boukhers PDF Video
15th Jan Context-dependent Classification Zeyd Boukhers PDF Video
22th Jan Data Transformation Zeyd Boukhers PDF Video
29th Jan Clustering Part 1 Zeyd Boukhers PDF Video
5th Feb Clustering Part 2 Raphael Menges PDF There will be no video recording.
12th Feb Exam


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
31st Oct Organizational Notes Akram Sadat Hosseini, Qusai Ramadan, and Raphael Menges Notes
7th Nov Machine Learning Fundamentals Qusai Ramadan
14th Nov Data Preprocessing and Visualization Raphael Menges
21st Nov k-Nearest Neighbors Akram Sadat Hosseini
28th Nov Naive Bayes Raphael Menges
5th Dec Decision Tree Akram Sadat Hosseini
12th Dec Decision Tree (Programming) Akram Sadat Hosseini
19th Dec SVM Qusai Ramadan
9th Jan Test Exam Akram Sadat Hosseini testexam.pdf
16th Jan Neural Network Qusai Ramadan
23rd Jan Neural Network (Programming) Raphael Menges
30th Jan Hidden Markov Chain Raphael Menges
6th Feb Clustering Qusai Ramadan


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

Release Date Deadline (9:00 AM!) Topic Sheets Remarks
28th Oct 4th Nov Pen and Paper: Machine Learning Fundamentals assignment01.pdf Alternative link, protected with same password as lectures. Task 1b) has been updated on 28th October, 5 p.m.
4th Nov 11th Nov Programming: Data Preprocessing and Visualization assignment02.pdf assignment02.csv Adapted the task descriptions to the notation of the lecture. PDF has been updated on 6th November, 3 p.m.
11th Nov 18th Nov Pen and Paper / Programming: k-Nearest-Neighbors assignment03.pdf Updated on 10th November, 9 p.m. Additional info: You may also import and use the module "math"
18th Nov 25th Nov Pen and Paper: Naive Bayes assignment04.pdf
25th Nov 2nd Dec Pen and Paper: Decision Tree assignment05.pdf
2nd Dec 9th Dec Programming: Decision Tree assignment06.pdf assignment06.ipynb
9th Dec 16th Dec Pen and Paper: SVM assignment07.pdf
6th Jan 13th Jan Pen and Paper: Neural Network assignment08.pdf
13th Jan 20th Jan Programming: Neural Network assignment09.pdf (.ipynb inside)
20th Jan 27th Jan Pen and Paper: Hidden Markov Chain assignment10.pdf Instead of PCA, we decided to give a HMM task. Thus the change of topic for this assignment.
27th Jan 3rd Feb Pen and Paper: Clustering assignment11.pdf


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