Summer Semester 2024

Automated Machine Learning

Course type: Flipped-classroom Lecture + Exercise
Time: Mondays, 14:00 - 16:00
Location: HS 00 036 SCHICK - SAAL (G.-Köhler-Allee 101)
Organizers: Frank Hutter , Heri Rakotoarison , Neeratyoy Mallik , Eddie Bergman , Johannes Hog , Martin Mráz, Steven Adriaensen , Noor Awad , André Biedenkapp
Web page: HISinOne

First Lecture

Date : Monday, April 15, 2024, 14:15.
Requirement for attending : The Overview lecture from the course website (password to be sent via HISinOne email and first in-person lecture).


Applying machine learning (ML) and in particular deep learning (DL) in practice is a challenging task and requires a lot of expertise. Among other things, the success of ML/DL applications depends on many design decisions, including an appropriate preprocessing of the data, choosing a well-performing machine learning algorithm, and tuning its hyperparameters, giving rise to a complex pipeline. Unfortunately, even experts need days, weeks, or even months to find well-performing pipelines and can still make mistakes when optimizing their pipelines. The course will discuss meta-algorithmic approaches to automatically search for, and obtain well-performing machine learning systems using automated machine learning (AutoML). Such AutoML systems allow for faster development of new ML/DL applications, require far less expert knowledge than doing everything from scratch and often even outperform human developers. In this lecture, you will learn how to use such AutoML systems, develop your own systems, and understand ideas behind state-of-the-art AutoML approaches.


We strongly recommend that you know the foundations of

  • machine learning (ML)
  • and deep learning (DL)

We further recommend that you have hands-on experience with:

  • Python (3.8+)
  • machine learning (scikit-learn)
  • deep learning (PyTorch)

The participants should have attended at least one other course for ML and DL in the past.


The lectures are partitioned into several parts, including:

  • Hyperparameter Optimization
  • Bayesian Optimization for Hyperparameter Optimization
  • Neural Architecture Search
  • Dynamic Configuration
  • Analysis and Interpretability of AutoML
  • Algorithm Selection/Meta-Learning


The course will be taught in a flipped-classroom style. We will meet weekly once for a lecture, and once weekly (optionally) for exercise sessions.

Every week, there will be a new exercise sheet. Most exercises will be practical, and involve programming in Python and teamwork (teams of up to 3 students!) so that you learn how to apply AutoML in practice.

Lecture: Mondays , 14 - 16

Exercise sessions [optional] (hybrid format) : Thursdays , 14-16

Exercise submission deadline [strict] (via Github) : Tuesdays, 23:59

Course material : The links to access the GitHub classroom repositories, videos with subtitles, and extra lecture materials will be made available on this page . (The password to that page will be announced in the first session and via HISinOne email).

The course will be taught in English.

MOOC content: The material is publicly available via the AI-Campus platform . Please register on AI-Campus to access the materials. Grading of the exercises will be done via GitHub classroom.

The lecture materials are open-sourced via .

Foundations of Artificial Intelligence

Course type: Live Lectures + (optional) Exercises
Time: Tuesday 10:00 - 12:00, Friday 10:00 - 12:00
Location: HS 00 036 SCHICK - SAAL (G.-Köhler-Allee 101)
Organizers: Joschka Bödecker , Tim Welschehold , Steven Adriaensen
Web page: ILIAS

Kickoff: The first lecture will take place on Tuesday 16.04. There is nothing you need to prepare. During this lecture, we will give you an overview of the course content, its organization, and the history of AI. The first exercise sheet will be released on Friday 19.04 and is due for submission before Friday 26.04, 8:00 am (optional, to receive feedback).

Course Content: This course will introduce basic concepts and techniques used within the field of Artificial Intelligence. Among other topics, we will discuss:

  • Introduction to and history of Artificial Intelligence
  • Agents
  • Problem-solving and search
  • Board Games
  • Logic and knowledge representation
  • Planning
  • Representation of and reasoning with uncertainty
  • Machine learning
  • Deep Learning

Lectures will roughly follow the book: Artificial Intelligence a Modern Approach (3rd edition)

Organization : There are two lecture slots every week, on Tuesday and Friday (10:15 - 11:45). During these slots, there will be live (in-person) lectures. These will be in-person only (not hybrid), but recordings will be made available on ILIAS.

Every week, we will also release an exercise sheet (on Friday) to be submitted before next Friday (8:00am). Your submissions will not be graded, but you will receive feedback from our tutors. On Friday, after the live lecture, one of our tutors will present the master solution and answer any questions you may have. Participation in these weekly sessions and exercises is optional. The only requirement for passing the course is passing the final exam (mode: written, in-person, open book) which will take place on 06.09.2024 at 9:00am.

If you have any questions, please post in the ILIAS forum or contact us

Laboratory: Deep Learning Lab

Welcome to the Deep Learning Lab a joint teaching effort of the Robotics (R) , Robot Learning (RL) , Neurorobotics (NR) , Computer Vision (CV) , and Machine Learning (ML) Labs. For more details check the following link: