Worth the hype?
Machine Learning is a subdomain of artificial intelligence and enables technical systems like computers to learn automatically from experiences in order to improve themselves step by step. The more data points you add and the more often the learning process takes place, the better the models perform.
Complex algorithms form the basis for automatic learning processes. These algorithms can be described as a kind of construction manual with sequences of steps and rules by which a problem is solved. In order to derive suitable solutions, algorithms are applied to existing data sets and recognize patterns and rules independently. The gained knowledge can be generalized and applied to new, unknown data sets, to make predictions for example.
By this means, machine learning models generate knowledge based on already gained experience. This characteristic offers enormous potential and distinguishes machine learning from traditional programming. In traditional programming, the rules are programmed and applied by humans manually to form algorithms that generate solutions for problems. In age of digitalization and big data, however, the amount of data is far too large to develop suitable algorithms for every problem manually.
Therefore, more and more organizations are using “learning machines” to work more efficiently and creatively.
Which types of machine learning are there?
In order to solve various problems with machine learning, different types of independent learning have developed. There are three basic types:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
What is Supervised Learning?
Supervised learning uses already known data with example models defined in advance. This data is first labeled with the solution by humans according to a known logic, before it is fed into the machine learning models. After that, the algorithm learns to recognize patterns and correlations based on a training set and receives a basic knowledge of how it should make decisions for new data sets.
The better the quality of the training set is, the more reliable the answer will be delivered by the algorithm. In practice, supervised learning is used for classifications or regressions. In this way, for example, customers can be assigned to specific groups of buyers based on their purchasing behavior or the electricity consumption of a household can be predicted by using historical data.
Supervised Learning vs. Unsupervised Learning
Unsupervised learning, in contrast to supervised learning, does not get solutions that have already been labelled. The algorithm is assigned to independently recognize, order and differentiate structures within data based on their values. Thus, interesting or hidden groups and patterns that would have remained hidden to humans, can be recognized automatically.
However, the groups found must be classified and evaluated by humans afterwards because the algorithm does not provide any reason why the data was grouped in this way. Unsupervised learning is used, for example, in speech recognition to derive speech habits for assistance systems such as Siri or Alexa. In addition, functional problems in machines can be solved by unsupervised methods for detecting anomalies or for predictive maintenance.
What is Reinforcement Learning?
Reinforcement learning is a special form of machine learning. The algorithm interacts with its environment and learns by trial and error. However, it is unclear which kind of action is correct in which situation. For this purpose, a reward system and a cost function are defined.
They either reinforce actions with additional points or punish them by deducting points. The algorithm now needs to develop a strategy to solve the problem independently by maximizing the number of points in order to provide the best result. Reinforcement learning is used in practice, for example, in parking assistants that recognize objects in the environment. According to these objects the parking assistant displays the optimal path to park the car.
Other applications are various optimization problems, for example in logistics or the energy industry.
What are the applications of machine learning?
Machine learning already determines our everyday life, even if we often do not notice it directly. Whether it is navigating through the city, performing the same activities which nevertheless require new decisions, or optimizing complex automated processes in the industry. The possible applications of machine learning are manifold.
In the field of autonomous driving and the mobility of the future, the data collected from sensors such as radars or cameras, form an extensive database. Machine learning algorithms fulfill various tasks in this context. For example, autonomously driving cars must detect and identify objects in their environment. Based on that, algorithms need to predict whether these objects will move in the next few seconds and if yes, in which direction.
In the field of medicine, blood tests, X-rays or medical reports produce vast amounts of data every single day. With the help of machine learning algorithms, similarity analyses of patient data can help to identify patterns and correlations in disease progressions. In addition, algorithms are now even capable of recognizing precursors of cancer cells based on techniques for the image recognition and by that improving the quality of early detection.
Another field of application is marketing for an individualized customer communication. In addition to geographical or time-related data, the purchasing behavior of customers also provides information on preferences and interests. Based on this information, behavioral patterns can be found, and target groups can be segmented. Machine learning algorithms also help to optimize personalized customer communication at the right time. Loyalty and customer satisfaction can be easily increased by individual products and measures matched to the customer.
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Sources
https://www.ibm.com/topics/machine-learning
IBM (2022): “What is machine learning?” [03.01.2023]
