The course on "Automated Machine Learning" addresses the challenge of designing well-performing Machine Learning (ML) pipelines, including their hyperparameters, architectures of deep Neural Networks and pre-processing. Future ML developers will learn how to use and design automated approaches for determining such ML pipelines efficiently. The course is designed either to be taken as a MOOC or can be offered by universities in a Blended Learning format with face-to-face and online phases.

• In Hyperparameter Optimization, the hyperparameter settings of a given Machine Learning algorithm are optimized to achieve great performance on a given dataset.

• In Neural Architecture Search, the architecture of a Neural Network is tuned for its predictive performance (or in addition inference time or model size) on a given dataset.

• As AutoML optimizers, approaches such as Bayesian optimization, evolutionary algorithms, multi-fidelity optimization and gradient-based optimization are discussed.

• Via Dynamic & Meta-Learning, useful meta strategies for speeding up the learning itself or AutoML are learned across datasets.

   

By the end of the course, you‘ll be able to…

• identify possible design decisions and procedures in the application of ML. 

• evaluate the design decisions made.

• implement efficient optimizers for AutoML problems, such as hyperparameter optimization and neural architecture search.

• increase the efficiency of AutoML via a multitude of different approaches.

• Basics in Machine Learning (ML) and Deep Learning (DL) 

• First experiences in the application of ML & DL  

• Python or R as programming language  

• Recommended but optional: Basics of Reinforcement Learning