Machine learning is a branch of artificial intelligence. By recognizing patterns in existing data, IT systems are able to independently find solutions to problems.
Machine learning is a sub-area of artificial intelligence. Machine learning enables IT systems to recognize patterns and laws on the basis of existing data and algorithms and to develop solutions. Artificial knowledge is generated from experience. The knowledge gained from the data can be generalized and used for new problem solutions or for the analysis of previously unknown data.
In order for the software to learn independently and find solutions, it is necessary for people to act beforehand. For example, the systems must first be supplied with the data and algorithms relevant for learning. In addition, rules must be established for the analysis of the data stock and the recognition of patterns. If suitable data is available and rules are defined, systems with machine learning can, among other things, do the following:
- Find, extract and summarize relevant data,
- Make predictions based on the analyzed data,
- Calculate probabilities for certain events,
- Adapt themselves to developments independently and
- Optimize processes based on recognized patterns.
The Different Types of Machine Learning
Algorithms play a central role in machine learning. They are responsible for recognizing patterns and generating solutions and can be divided into different learning categories.
- supervised learning
- unsupervised learning
- partially supervised learning
- encouraging learning
- active learning
While in supervised learning example models must be defined and specified in advance in order to assign the information appropriately to the model groups of the algorithms, in unsupervised learning the model groups are automatically formed on the basis of independently recognized patterns.
Partially supervised learning is a mixture of both methods. Encouraging learning is based on rewards and punishments. This interaction tells the algorithm how to react to different situations. This way of learning is very similar to human learning.
Finally, active learning allows the algorithm to request the desired results for certain input data. In order to minimize the number of questions, the algorithm itself first selects relevant questions with high result relevance.
Depending on the respective system, the database can be available offline or online and can be repeatable or only available once for machine learning. A further distinguishing feature of machine learning is the simultaneous presence of input and output pairs or their chronologically shifted development. Depending on the type, this is referred to as batch learning or sequential learning.
Application Examples for Machine Learning
Machine Learning has a very wide range of application possibilities. In the Internet environment, machine learning is used for the following functions, for example:
- Independent detection of spam mails and development of suitable spam filters
- Speech and text recognition for digital assistants
- Determination of the relevance of web pages for search terms
- Detection and differentiation of Internet activity from natural persons and bots
Other areas of application for machine learning are image and face recognition, automatic recommendation services or the automatic recognition of credit card fraud.
Big Data as a Driver of Machine Learning
The development of Big Data technology has also given an enormous boost to machine learning. Since machine learning requires large amounts of data and efficient processing, big data systems are the ideal basis for this type of learning. With the help of Big Data, both structured and unstructured data can be analyzed quickly and with relatively little hardware effort and fed to the learning algorithms.
Distributed computer structures and particularly fast database systems are used for machine learning. Artificial neural networks are also used, which function according to the model of the human brain.
