Machine Learning and Data Mining

Wintersemester 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 raphaelmenges@uni-koblenz.de 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

ECTS: 6

Exam:

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 12

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 for the exam, you will receive everything via email if you are registered. Otherwise, please check the web page regularly.

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 (boukhers@uni-koblenz.de)

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.

Tutorials

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

Assignments

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

Lehrende

Dr. Zeyd Boukhers

• boukhers@uni-koblenz.de
• Wissenschaftlicher Mitarbeiter
• B 114
• +49 261 287-2765

Raphael Menges

• raphaelmenges@uni-koblenz.de
• Wissenschaftlicher Mitarbeiter
• B 104
• +49 261 287-2862

Akram Sadat Hosseini

• Alumnus