Machine Learning

Introduction to Machine Learning

General Information

Lecturer: Prof. Dr. Benno Stein
Lab advisors: Michael Völske
Student tutors: Le Anh Phuong
Workload: 2 SWS Lecture, 1 SWS Lab
ECTS Credits: 4.5 for CS4DM, CSM, MI, and HCI before 2019; 6 for DE and HCI since 2019
Lecture: Th. 9:15am - 10:45am (weekly starting October 24th). Marienstraße 13 C - Hörsaal C
Lab: Th. 11:00am - 12:30pm (biweekly starting October 24th). Marienstraße 13 C - Hörsaal C
Examination: to be announced


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. Details are printed at the top of each exercise sheet.

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. The deadline and the exercises to be submitted are printed on each exercise sheet. Note also the instructions printed at the top of the exercise sheet, and pay special attention to which exercises you must submit.

  • 24.10.2019
    Lab class introduction; Exercise 1 [exercises]
    Moodle introduction [slides]
    Group selection open [moodle]

  • 31.10.2019: no lecture (public holiday)

  • 07.11.2019
    Python basics [notebooks] (external link)
    Standard library, numpy, matplotlib [notebook] [slides]

  • 11.11.2019 11:00 am: submission deadline for exercise 1 [submission]

  • 14.11.2019
    Discussion of exercise 1
    Exercise 2 [exercises]

  • 25.11.2019 11:00 am: submission deadline for exercise 2 [submission]
    Last chance to change group selection [moodle]

  • 28.11.2019
    Discussion of exercise 2
    Exercise 3 [exercises]

  • 09.12.2019 11:00 am: submission deadline for exercise 3

  • 12.12.2019
    Discussion of exercise 3
    Exercise 4 [exercises]

  • 06.01.2020 11:00 am: submission deadline for exercise 4

  • 09.01.2020
    Discussion of exercise 4
    Exercise 5 [exercise]

  • 20.01.2020 11:00 am: submission deadline for exercise 5

  • 23.01.2020
    Discussion of exercise 5
    Exercise 6 [exercise]

  • 03.02.2020 11:00 am: submission deadline for exercise 6

  • 06.02.2020
    Discussion of exercise 6
    Exam preparation




Machine Learning:

  • Christopher M. Bishop. Pattern Recognition and Machine Learning. 2nd edition, Springer 2007.
  • Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone. Classification and Regression Trees. CRC Press reprint, 1998.
  • Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning. 2nd edition, Springer, 2009.
  • Tom Mitchell. Machine Learning. 1st edition, McGraw-Hill, 1997.
  • Vladimir Vapnik. The Nature of Statistical Learning Theory. 2nd edition, Springer 2000.

Data Mining:

  • David Hand, Heikki Mannila, Padhraic Smyth. Principles of Data Mining. Bradford, 2001.
  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. 1st edition, Addison Wesley, 2005.
  • Ian H. Witten, Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. 3rd edition, Morgan Kaufmann, 2011.
  • Anil K. Jain. Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters, Vol. 31, Issue 8, 2010.