ELLIS Summer School on AutoML

Scope

AutoML has become a cornerstone in the toolkit of many developers and researchers. With the rise of foundation models, AutoML's potential has expanded even further, enabling smarter, more powerful, and scalable solutions. Although there is exponentially growing interest in the intersection of AutoML and foundation models, they are rarely taught at universities, leaving a significant gap between cutting-edge research and disseminated knowledge. This school consists of introductory, state-of-the-art, and application lectures for AutoML and Foundation Models covering core topics such as:

• Hyperparameter optimization and model selection

• Applying and scaling foundation models

• Automated model compression, and pruning

• LLMs for efficient experimentation

• Explainability methods

• ... and many more