Machine Learning

Introduction to Machine Learning

General Information

Lecturer: Prof. Dr. Benno Stein
Lab advisors: Michael Völske, Janek Bevendorff
Student tutors: Ronald Kurniawan
Workload: 2 SWS Lecture, 1 SWS Lab
ECTS Credits: 4.5 or 6 depending on degree programme
Lecture: Th. 9:15am - 10:45am (weekly starting October 21st). Marienstraße 13 C - Hörsaal A
Lab: Th. 11:00am - 12:30pm (biweekly starting October 28th). Marienstraße 13 C - Hörsaal A
Examination: TBD


Lab Class

Active participation in the lab class is a requirement for admission to the final exam. Depending on the number of ECTS credits you receive for the course, there is a different minimum score you must attain in the exercises.

To submit your solutions to the lab class exercises, you must enrol in the Moodle course for the lecture. The required enrolment key will be announced in the first lab class. Working in groups of up to 3 students is encouraged -- once you have formed a group, each member must select the same group number in Moodle.

  • 28.10.2021
    Lab class introduction
    Exercise sheet 1: Learning Problems, Regression [worksheet]
    Group selection open [moodle]
    Tutorial: Python basics. [notebook]
    Tutorial: Jupyter. [notebook]

  • 08.11.2021 23:59: submission deadline for exercise sheet 1 [moodle]

  • 11.11.2021
    Discussion of Exercise sheet 1
    Exercise sheet 2: Concept Learning [worksheet] [additional test cases]
    Tutorial: scientific Python. [notebook]

  • 22.11.2021 23:59: submission deadline for exercise sheet 2 [moodle]
    Last chance to change group selection [moodle]

  • 25.11.2021
    Discussion of exercise sheet 2
    Exercise sheet 3: Linear Models [worksheet] [notebook] [training-data] [test-data]
    Tutorial: Regularized basis expansion [notebook]

  • 06.12.2021 23:59: submission deadline for exercise sheet 3 [moodle]

  • 09.12.2021
    Discussion of exercise sheet 3
    Exercise sheet 4: Neural Networks [worksheet] [training-data] [test-data]
    Tutorial: Implementing the multilayer perceptron [notebook]

  • 03.01.2022 23:59: submission deadline for exercise sheet 4

  • 09.01.2022
    Discussion of exercise sheet 4
    Exercise sheet 5: Decision trees and statistical Learning [worksheet]

  • 17.01.2022 23:59: submission deadline for exercise sheet 5 [moodle]

  • 20.01.2022
    Discussion of exercise sheet 5
    Exercise sheet 6: Deep learning [worksheet]

  • 31.01.2022 23:59: submission deadline for exercise sheet 6 [moodle]

  • 03.02.2022
    Discussion of exercise sheet 6
    Exam Q & A


Please find the literature in the lecture slides.