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AutoML
AI Campus Original
AutoML - Automated Machine Learning
Start date
anytime
Duration
14 weeks à 8 hours
Language
English

Overview

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

Who is offering this course?

Prof. Dr. Marius Lindauer
Prof. Dr. Marius Lindauer
Leibniz Universität Hannover
Prof. Dr. Frank Hutter
Prof. Dr. Frank Hutter
Universität Freiburg
Bernd Bischl neu
Prof. Dr. Bernd Bischl
Ludwig-Maximilians-Universität München
Dr. Lars Kotthoff
Prof. Dr. Lars Kotthoff
University of Wyoming
Dr. Janek Thomas
Dr. Janek Thomas
Fraunhofer IIS

What else do I need to know?

Learning format:
Blended-Learning-Format
Level:
Advanced
License
CC-BY-SA 4.0