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 by means of 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, to develop your own systems and to 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 (3.6+)
  • machine learning
  • deep learning

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


The lectures are partitioned in 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 combined Q/A session and an exercise. Roughly every week, there will be a new exercise sheet. Most exercises will be practical, involve programming in python and teamwork (teams of up to 3 students!) so that you learn how to apply AutoML in practice.

Lecture/Exercise: Wednesday 12:00 - 14:00

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 links to access the GitHub classroom repositories are available here. (The password to that page will be announced in the first session). The course will be taught in English.


Student conference:

“Students groups of three write a short report on one of three potential topics, peer-review the reports and present their work in a virtual poster session.”

  • Report: 4 pages + #students pages appendix
    • Contributions of each student needs to be described for the report
    • Paper template will be provided
    • Will be organized on
  • Peer review:
    • Each student has to write a review for one other paper
    • We will provide some reviewing guidelines
  • Poster Conference:
    • Jointly with Hannover
    • Online in
    • Oral presentation of your poster during a predefined time-slot
  • 20.07.2022: Project handout
  • 31.08.2022: Report Deadline
  • 09.09.2022: Review Deadline
  • 13.09.2022 (16:00 CEST): Poster Submission Deadline (a template is available here)
  • 15 - 16.09.2022: Poster Session
  • 23.09.2022: Final Deadline (incorporate feedback from the poster session)

Conference Schedule

DayTime SlotPoster RoomPaper IDs
Thursday 15.09.2209:00 - 10:45Room 1 9, 16, 20, 24, 25
Thursday 15.09.2211:00 - 12:30Room 2 6, 7, 11, 12, 27
Thursday 15.09.2213:30 - 15:30Room 3 2, 13, 14, 15, 28, 30
Friday 16.09.2209:00 - 11:30Room 1 1, 3, 4, 5, 17, 18, 19

Please make sure to be at your poster during the time-slot above. While it is not your turn to present a poster, you can visit the other posters and discuss the approaches with the presenters. You are expected to take part in the whole conference. If you're unsure of your paper ID, you can check on openreview.