AutoML ...

provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning. Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform manual tasks. As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.

Hyperparameter Optimization

Hyperparameter Optimization

... allows to automatically find well-performing hyperparameter settings of your machine learning algorithm (e.g., SVM, RF or DNN) on a given dataset.

Neural Architecture Search

Neural Architecture Search

... automatically determines an appropriate architecture of a neural network for a dataset at hand.

Meta-Learning

Meta-Learning

... aims add learning across datasets, e.g., warmstarting of HPO & NAS, learning of dynamic policies for hyperparameters settings, or learning to learn.