Labor: AutoML

Prof. Dr. rer. nat. Marius Lindauer
Übungsbetreuung:

Background

Machine Learning (ML) has achieved remarkable success in recent years. However, the choice of ML algorithms (SVM, random forest or deep neural network) and their hyper-parameters is yet another process of “learning”. e.g. , designing a well performed ML system requires a lot of expert knowledge and it is often the result of repeated “trial and error”. Even worse, the “no free lunch” theorem indicates that there is no single approach that works best across every task. Hence, the above tedious process will be repeated again and again facing new tasks. To alleviate the above problems, Automated Machine Learning (AutoML) is proposed to automate the design of the whole ML pipeline, including but not limited to the techniques mentioned above. In this practical lab course, you will learn to implement the main ideas of an AutoML system from scratch and how to apply AutoML to applications.

Requirements

Topics

Literature

Dynamics

This course will be in English only and fully virtual. At the beginning of the term, we will discuss and decide whether we will meet weekly for 4h or whether we will do the lab course in an one-week block at the end of the semester.