Vorlesung: Statistical Machine Learning

Quick Facts

Lecturer:Jan Peters
Teaching Assistants:Dorothea Koert, Svenja Stark, Hany Abdulsamad
Lectures:Wednesday, 13:30-17:00 in Room S103/226
 Friday, 03.05.2019, 09:50-11:30 in Room S202/C205 instead of Wednesday, 24.04.2019
 Friday, 21.06.2019, 13:30-15:10 in Room S202/C205 instead of Wednesday, 03.07.2019
 Monday, 22.07.2019, 16:00-18:00 in Room S202/C110 Discussion HW4
Office Hours:Monday, 14:00-15:00 in Room S202/E202 starting 13.05.2019
 Tuesday, 11.06.2019, 14:00-15:00 in Room S202/E203 instead of Monday, 10.06.2019 (Whitmonday)
 Or request by Email
TU-CAN:20-00-0358-iv Statistisches Maschinelles Lernen
Credits:6,0 ECTS (4 SWS)
Exam:Friday, 26.07.2019 13:00-15:00
 Room according to the first letter of your last name:
 A - G: S1 05/ 122
 H - M: S1 01/ A01
 N - Z: S2 06/ 030
Exam Review:Friday, 04.10.2019, 13:00-15:30 in Room S202/A126
Exam Repetition:Thursday, 12.3.2020, 14:30-16:30 in Room S1|03 07


As the World Wide Web keeps growing, computer science keeps evolving from its traditional form, slowly slowly becoming the art to create intelligent software and hardware systems that draw relevant information from the enormous amount of available data.

Why? Let's look at the facts: billions of web pages are at our disposal, videos with an accumulated time of 20 hours are uploaded every minute on Youtube and the supermarket chain Walmart alone performed more than one million transactions per hour, creating a database of more than 2.5 petabytes of information. John Naisbitt has stated the problem very clearly:

"We are drowning in information and starving for knowledge."

In the future of computer science, machine learning will therefore be an important core technology. Not only that, machine learning already is the technology which promises the best computer science jobs. Hal Varian, the Chief Engineer of Google in 2009 depicted it like this:

"I keep saying the sexy job in the next ten years will be statisticians and machine learners. People think I am joking, but who would have guessed that computer engineers would have been the sexy job of the 1990s? The ability to take data, to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it, that is going to be a hugely important skill in the next decades. "

Accordingly, this lecture serves as an introduction to machine learning. Special emphasis is placed on a clear presentation of the lectures contents supplemented by small sample problems regarding each of the topics. The teacher pays particular attention to his interactions with the participants of the lecture, asking multiple question and appreciating enthusiastic students.


The course gives an introduction to statistical machine learning methods. The following topics are expected to be covered throughout the semester:

  • Probability Distributions
  • Linear Models for Regression and Classification
  • Kernel Methods, Graphical Models
  • Mixture Models and EM
  • Approximate Inference
  • Continuous Latent Variables
  • Hidden Markov Models


Math classes from the bachelor's degree, basic programming abilities, introductory classes to computer science.

Moodle Class

All further information and announcements regarding the lecture will be made public over the Moodle system of the computer science department: https://moodle.informatik.tu-darmstadt.de/course/view.php?id=595

Academic Honesty Policy

We grade homework such that you get early feedback on your performance. If you copy, you are dishonest, do waste our time and effort, do worse on the exam and violate all proper academic conduct. We will report you to the dean of studies office (Studiendekanat) and not count any of your bonus points.

To see information on the teaching material, homeworks and final exam info, please login with your TU-ID!

Teaching Staff

Lectures will be held by Jan Peters and additionally supervised by Hany Abdulsamad, Dorothea Koert and Svenja Stark.

Jan Peters heads the Intelligent Autonomous Systems Lab at the Department of Computer Science at the TU Darmstadt. Jan has studied computer science, electrical, control, mechanical and aerospace engineering. You can find Jan Peters in the Robert-Piloty building S2 | 02 room E314. You can also contact him through mail@jan-peters.net.

Dorothea Koert joined the IAS Lab as a Phd student in May 2016. She is working on autonomous skill learning within the SKILLS4ROBOTS project. You can contact her via email doro@robot-learning.de.

Svenja Stark joined the IAS Lab as a Phd student in December 2016. She is working on adaptive skill libraries and skill comparison. You can contact her via email svenja@robot-learning.de.

Hany Abdulsamad joined the Intelligent Autonomous System lab in April 2016 as a PhD student. His research interests include optimal control, trajectory optimization, reinforcement learning and robotics. During his Phd, Hany is working on the SKILLS4ROBOTS project with the aim of enabling humanoid robots to acquire and improve a rich set of motor skills. You can contact him by email at hany@robot-learning.de .

For further inquiries do not hesitate to contact us immediately!


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