Welcome to the course
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.
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
How do I participate in the course?
Course Materials
All course materials can be found on the ki-campus. To participate in the course, you need to register and enroll yourself into the AutoML course there.
Communication
For communication we have a discord channel.
Copyright
This material is offered under a Creative Commons CC BY-SA 4.0 license.
This course is offered in the xAIM's master's program. The course is co-financed by the European Commission instrument "Connecting Europe Facility (CEF) Telecom" through the project "eXplainable Artificial Intelligence in healthcare Management project" (2020-EU-IA-0098), grant agreement number INEA/CEF/ICT/A2020/ 2276680.
