Dear Master students of Physics,

have you wondered how to make sense of structured, high-dimensional, noisy data? How to model systems involving complicated dynamics? How to choose experimental parameters to maximise information gained from them?

Dear Master students of Mathematics,

have you wondered what links statistics, probability theory and numerics? How to extend propositional logic to uncertain statements? How to formalise uncertainty, information and learning? And how much information it takes to describe one function?

Dear Master students of Computer Science,

have you wondered what all the "big data" hype is about? What is an "intelligent", an "autonomous", a "learning" system? Is there one big theory behind everything from neural networks, to support vector machines, to K-means?

Find out about all this and more, at the new lecture course

Intelligent Systems I
this Summer Semester, Thursdays, 8 c.t. to 10
(first lecture on 16 April)
in room A104, auf dem Sand 1

Brought to you by the new Max Planck Institute for Intelligent Systems, the course will cover basic probability theory, and a host of applied inference, learning and decision algorithms, all derived and understood within the fundamental framework of uncertainty. There are no formal prerequisites, except basic linear algebra and calculus. The lecture will be complemented by a group exercise session.

We are looking forward to seeing you there. No matter which of the above groups you belong to, we speak your language:

Dr. Michael Hirsch, Dipl. Phys.
Dr. Jonas Peters, Dipl. Math., M.A.St.
MPI for Intelligent Systems
Department of Empirical Inference


Teaching assistants:
Behzad Tabibian, MSc Information Science
Carl Johann Simon-Gabriel

Slides and additional material

Lecture 1 - 16 April 2015 | Introduction

Important note: Please note that the first exercise sheet is due at next week's lecture, i.e. Thursday, 23. April. However, for getting the 4 points of the first exercise, please complete it before next weeks's first exercise session, which will take place on Tuesday, 21. April in room A302.

Lecture 2 - 23 April 2015 | Probability Theory, SVM

Lecture 3 - 30 April 2015 | Linear Regression

Lecture 4 - 7 May 2015 | Cross-Validation & Probabilistic Regression

Lecture 5 - 21 April 2015 | LASSO & towards Gaussian Process Regression

Lecture 6 - 11 June 2015 | Nonparametric Regression & Introduction to Causality

Lecture 7 - 18 June 2015 | Causality

Lecture 8 - 25 June 2015 | Causality

Lecture 9 - 2 July 2015 | Kernels

Lecture 10 - 9 July 2015 | Dimensionality Reduction

Lecture 11 - 16 July 2015

Exam - 23 July 2015

Email List

For receiving and posting messages to the email list you don't need a Google account. However, if you want to have access to the forum it seems that you need to sign up for a Google account unfortunately. Please note that you don't need a Google email account, but you can sign up with any other email address as well. Here is the link to the Google group associated with this course: