The success of machine learning crucially relies on human machine learning experts, who construct appropriate features and workflows, and select appropriate machine learning paradigms, algorithms, neural architectures, and their hyperparameters. Automated Machine Learning (AutoML) is an emerging research area that targets the progressive automation of machine learning, which uses machine learning and optimization to develop off-the-shelf machine learning methods. It targets both ML researchers and non-ML experts, easing and enabling the use of machine learning algorithms. AutoML covers a broad range of subfields, including hyperparameter optimization, neural architecture search and meta-learning. This tutorial will cover the methods underlying the current state of the art in this fast-paced field and also contain hands-on exercises using state-of-the-art AutoML tools.
- Introduction & Motivation
- Automated Machine Learning via Hyperparameter optimization
- Neural Architecture Search
- Meta-Learning & Learning to Learn