Machine Learning (Spring 2020)


Official course description

Machine learning (ML) is about algorithms which are fed with (large quantities of) real-world data, and which return a compressed “model” of the data. An example is the “world model” of a robot: the input data are sensor data streams, from which the robot learns a model of its environment — needed, for instance, for navigation. Another example is a spoken language model: the input data are speech recordings, from which ML methods build a model of spoken English — useful, for instance, in automated speech recognition systems. There exist many formalisms in which such models can be cast, and an equally large diversity of learning algorithms. However, there is a relatively small number of fundamental challenges which are common to all of these formalisms and algorithms. The lecture introduces such fundamental concepts and illustrates them with a choice of elementary model formalisms (linear classifiers and regressors, radial basis function networks, clustering, neural networks, hidden Markov models). Furthermore, the lecture also provides a refresher of required mathematical material from probability theory and linear algebra.


Primary text:
Hastie, Tibshirani, Friedman: “The Elements of Statistical Learning” (Second Edition), Springer

Recommended reading:
Shalev-Shwartz, Ben-David: “Understanding Machine Learning: From Theory to Algorithms”, Cambridge University Press

Further useful references for the math background:
Linear algebra and probability reviews available at 


The grades for this lecture will be determined as follows:

  • final exam (100 %)

There will be no other formal requirements.

Final exam

All rules, times, etc. are consolidated in the Final exam announcement.

Lecture style, tutorials, homeworks and further information

Online teaching

Online classes are carried out as follows:

  1. Video recordings of the class content that would have been normally presented in the lecture slot. The videos can be either watched via the embedded player or downloaded by clicking on the name of the video.
  2. Online quizzes that can be carried out and repeated at any time.
  3. The slides that were uploaded for each lecture anyway.
  4. Questions & Answer Video-Conferencing sessions

The Video conferencing sessions take place in Microsoft Teams in the respective course on

  • Wednesdays, starting at 9:00
  • Thursdays, starting at 17:30

The instructor will keep the meeting running for at least ten minutes. If no student shows up in these ten minutes, the meeting is stopped.

Online tutorials

  • Offered via video conferencing in MS Teams in the “Team” of this lecture
  • Weekly tutorial classes offered by TAs:
    1. Mondays, 15:45-17:00
    2. Wednesdays, 17:15-18:30
  • Content:
    • Repetition and discussion of lecture content
    • Discussion of upcoming and graded homework
  • No mandatory attendance.
    → attendance highly recommended in order to be successful



  • one assignment sheet per week, published on moodle
  • contents of each assignment sheet:
    • ∼3 tasks: theory (manually computing predictor, proving, …)
    • ∼1 task: programming (implementing ML algorithms)
      → programming language C/C++


  • weekly deadline: Friday, 12:00 (noon)
  • submission format:
    • theory: via moodle
    • programming: via moodle
  • submissions in groups of 1 – 3
    → depending on class size and homework participation
    → might be subject to adjustments

Grading (of non-mandatory homeworks)

  • exercises graded with points by TAs
  • The points that students receive for their homework will have no influence on the final grade, i.e. doing the exercises is not mandatory.
  • However: Students that are not able to achieve at least 50% of the points from the exercises should expect that they have not got enough training in the content and therefore will most likely have issues in the final exam.

Code demos

Lecture content

Content until March 12 (i.e. in-person teaching)

Material for March 18

Material for March 19

Material for March 25

Material for March 26

Material for April 1

Material for April 2

Material for April 15

Material for April 16

Material for April 22

Material for April 23

Material for April 29

Material for April 30

Material for May 6

Material for May 7

Material for May 13

Final exam discussion lecture (May 14)

The final lecture will be again a live lecture without lecture recording. It will take place in the original lecture slot.