Machine Learning Catalogue
Methods and Use Cases
This is a catalogue of machine learning methods. It is intended for use when you wish to obtain a list of methods that would be appropriate for a specific use case or when you wish to find out about a specific technique that is mentioned in an article, program or lecture.
Categories
The Machine Learning Catalogue consists of multiple categories.
Algorithm
A machine learning algorithm describes a procedure to train a machine learning model based on training or input data. This covers descriptions of the underlying architectural patterns and building blocks within the models as well, although they are not prediction models themselves.
Use Case
A use case describes capabilities of machine learning models to address and resolve typical business challenges.
Supporting Technique
Supporting techniques are procedures that help improve the performance of a machine learning algorithm.
Data Type
Data types describes the structure and characteristics of information. Understanding data types is crucial to select suitable algorithms that fit both the use case and available data.
Learning Style
Learning styles describe the way a machine learning model learns from the available data to create a machine learning model.
Miscellaneous
Miscellaneous terms form a glossary of relevant ideas in the context of machine learning that describe, e.g., general definitions, functional building blocks, internal procedures, or other popular terms.
Use Cases and Capabilities
Free Poster!
We have grouped the Generic Use Cases for Artificial Intelligence and put them together in a poster. The poster is available free of charge as a download and can be printed in DIN A1 format.
Contributors
The Machine Learning Catalogue is the result of the work of the following experts from msg Applied Technology Research.
