Roughly speaking, machine learning comprises a series of methods, that enable computers to adopt certain desired behaviours based on the training of given examples instead of programming the computer with hard rules. This approach is beneficial especially for complex problems, because clear rules or equations are rarely available or known in such cases.
Beside the realization of (self-)adaptive systems, machine learning methods can be used to uncover a system's underlying rules to describe its behaviour a posteriori. In this context machine learning methods are the basics for data mining.
In addition to Artificial Neural Networks, Classification and Regression Trees and Support Vector Machines, for example, are also part of the various machine learning methods.
- When does the application of Artificial Intelligence (AI) pay of?
- Artificial Neural Networks are Blackbox routines. Are you allowed to use them?
- What's so special about Artificial Neural Networks?
- How many data do you need for training an Artificial Neural Network?
- How to safeguard Artificial Neural Networks against unexpected extrapolation behaviour?
- Artificial Intelligence, Computational Intelligence, SoftComputing, Natural Computation - what's the difference?
- What's the difference between Machine Learning, Artificial Neural Networks, and Deep Learning?
- Why is the usage machine learning a successful measure against the "confirmation bias"?
- Why is adaptivity so important?
- What is an "intelligent system" and when is it allowed to talk about "Artificial Intelligence"?
- Optimization does not work, what can be done?
- Despite having excellent optimization results the real system fails totally – what went wrong?
- What does "robustness management" mean?
- What does "complexity management" mean?