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
- You should know the foundations and have hands-on experience with Machine Learning and Deep Learning, i.e., successfully attending at least one course on both of these.
- You should have very good Python programming skills; everything will be done in Python in this lab course.
- We strongly recommend that you successfully attended the AutoML lecture in the summer semester 2020.
