Machine Learning

What does "machine learning" mean, how can a computer "learn"?

The term "learning" is used in this context to refer to the fact that a machine uses new data in order to better up previous results. For sure the machine will not develop a brain or a physically similar organ. But for optimization or minimizing tasks, especially tasks including complex calculations and transformations, a machine controlled adaption is a useful and often indispensable tool. The basics for such tasks are often machine learning algorithms like artificial neural networks or clustering algorithms which run iterative trying to minimize errors in each calculation step.

Machine learning algorithms usually can be devided in two main groups:

• Supervised learning algorithms, in which the output is known and the rules are being trained by using the input and trying to minimize the error between the predicted output and the given result. Neuronal networks are here the most promising examples, they are called like that as they reflect the way our brains work: given some input the human brain learns by trying out and correct until it finds the perfect rule to explain the result.

• Unsupervised learning algorithms in which data is given to an algorithm which then tries to find pattern in the data. As an example think about astronomical data: if you can cluster the stellar data you could find a structure in it and learn about the past and future.

In addition modern algorithms can also be active learning algorithms, in which the input can be completed by additional requests for input trying to minimize the numbers of these additional inputs.

What are these algorithms needed for and why are they considered promising?

Machines have the capacity to calculate fast and storage is cheap nowadays, also in presicion they are unbeatable and often parallelize their tasks. As nearly unlimited data is available and often more data leads to better results (not always, an intelligent way to sort out data is one of the reasons for a data scientist!) machines and computer visualizations are essential reasons for machine supported analysis. In addition cloud computing and distributed systems improve the way data is collected, loaded and analyzed.
Internet of Things scenarios are considered the modern way to improve business processes and drive Industry 4.0 by collecting huge amounts of (sensor) data. To analyze those datasets machines are not only helpful, but necessary.