The course

Machine learning is currently one of the most influential subfields of Computer Science. With applications in numerous fields, including information technologies, medicine, physics, and finances, Machine Learning has an ever growing influence on science and society.

This course will take a foundational perspective and cover some of the mathematical principles and concepts that underlie machine learning algorithms. Topics will range from well-established results in learning theory, to current methods and research challenges. We will start with classic results from the 70's and 80's, such as Vapnik-Chervonenkis (VC) theory and the Probably-Approximately-Correct (PAC) framework, which lay the ground for a formal theory of automated learning. Building on this, we will present and analyze several popular machine learning methods, such as Nearest Neighbors, Boosting, and Support Vector Machines. We will also point to directions of current research.

This course invites students from all faculties and levels that enjoy mathematical rigor and are curious to gain a deeper understanding of machine learning. It will be most suitable for Masters and PhD students with a background in Mathematics or theoretical Computer Science (especially if you are looking for an exciting research topic). Be prepared for theorems and proofs :)

We are looking forward to seeing you there!

Required background: basic knowledge on probability and linear algebra.


February 13
A brief reminder. Tomorrow we will have a written exam. It starts at 8:15 and lasts until 9:45.
You can use any printed sources during the exam. In order not to fail the course, you need to score
at least 30 points for the exam.

February 9
Next tutorial (next Monday) will be devoted to Quastions-and-Answers session.
Next Tuesday we will have a written exam, which starts at 8:15.

January 26
Assignment 5 is out!
It is due Monday, January 30, 12:15pm.
Please hand it in at the beginning of the tutorial.

January 20
We propose to schedule the exam on the day of last lecture, which is February 14th.
The exam will be written, consisting of a set of problems on the topics covered in the course. The students will all sit in the lecture room and receive their test sheets. Within a limited amount of time the students will need to solve the problems, write down the solutions, and hand them to us.
Students are allowed to use any printed or written sources on paper, but no electronic devices.

January 13
There was a mistake in problem 2 of assignment 4. Now it is fixed.

January 10
Assignment 4 is out!
It is due Monday, January 16, 12:15pm.
Please hand it in at the beginning of the tutorial.

January 8
No tutorial on Monday, January 9!

December 11
Assignment 3 is out!
It is due Monday, December 19, 12:15pm.
Please hand it in at the beginning of the tutorial.

December 6
Next week, tutorial and lecture times are switched.
The lecture will be on Monday, Dec 12 at noon and the tutorial on Tuesday, Dec 13, at 8am.

December 4
Recall that there is no class and no tutorial this week.

November 15
Assignment 2 has been updated, some typos fixed.

November 14
Assignment 2 is out!
It is due Monday, November 21, 12:15pm.
Please hand it in at the beginning of the tutorial.

October 31
The first assignment is out!
It is due Monday, November 7, 12:15pm.
Please hand it in at the beginning of the tutorial.

Classes start on October 18. Tutorials start on October 24.


Material for individual lectures



Ruth Urner

Ilya Tolstikhin

Teaching assistants

Carl-Johann Simon-Gabriel

Paul Rubenstein

Rules of the game

Ruth Urner
Ilya Tolstikhin
MPI for Intelligent Systems
Department of Empirical Inference