AI Campus Original
AutoML - Automated Machine Learning
14 weeks à 8 hours
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For free


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.

Which topics will be covered?

  • 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.


What will I achieve?

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.

Which prerequisites do I need to fulfill?

  • 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

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Machine Learning