3rd AutoML
Fall School 2023
Motivation
By increasing the efficiency of ML-application development and supporting users in crucial design decisions, AutoML became a key approach in the toolkit of many developers and researchers. Although there is an exponentially growing interest in AutoML, AutoML is so far only rarely taught at universities and there is a large gap between the current state of the art in research and disseminated knowledge. The AutoML Fall School will cover core topics of AutoML, covering basics, state-of-the-art approaches and hands-on sessions. Enthusiastic AutoML experts will present their diverse views on AutoML to ML practitioners, developers, research engineers, researchers and students.
Key Features
Learn from world-leading
experts in AutoML
Hands-on sessions with
open-source packages
Talk to people --
Increase your network
Social Events
Invited Speakers
(University of Cambridge)
Scalable gradient-based optimisation of high-dimensional weight-update hyperparameters
Florian Pfisterer
(hema.to)
Fairness in Automated Decision Making
Philipp Stratmann
(Intel)
Neuromorphic Computing on Intel's Loihi 2
Alexander Tornede and Marius Lindauer
(Leibniz University Hannover)
AutoML in the Age of Large Language Models
Hands-On Sessions by
Carolin Benjamins & Dr. Marcel Wever
(Leibniz University Hannover & LMU Munich)
Topic: Tools for AutoML Research (Hydra and pyExperimenter)
(Albert-Ludwigs-Universität Freiburg)
Topic: The Future of Auto-Sklearn: A Modular AutoML Framework?
In the meantime, join our AutoML MOOC
... available for free at ai-campus.org and a perfect opportunity for getting a basic background in AutoML before attending our AutoML Fall school.
Organized by Members of
