Machine learning is a branch of computer science that allows an intelligent machine to improve its performance over time, the acquisition of new data and through the analysis of the results obtained with the choices made previously.
Machine learning algorithms, the story
The first big name related to machine learning is that of Alan Turing, who was the first to hypothesize the need for specific algorithms to make machines able to learn. The technological evolution in this field allows us today to have various modes of machine learning, which are distinguished by the algorithms on which they are based and for the purposes to be achieved.
Machine learning algorithms, the types
In particular, based on the type of algorithm used and therefore the way through which the machine learns and accumulates data, it is possible to identify three large machine learning systems:
- Supervised learning
- Unsupervised learning
- Learning with reinforcement
Supervised learning consists in providing the intelligent machine with a set of specific notions, models and examples that allow it to have a base of information to draw on to provide the best answer to every question to be solved. Algorithms that make use of supervised learning are used in many areas, from medical to speech identification: they, in fact, have the ability to make inductive hypotheses, that is, hypotheses that can be obtained by scanning a series of specific problems to obtain a suitable solution to a general problem.
Unsupervised learning is characterized by the fact that the machine uses the data provided without having examples of their use and therefore without having any kind of knowledge about the expected results. In this case, unlike the previous one, the machine is required to collect the received information, organize it and deduce its usefulness.
Finally, learning by reinforcement, the most complex model to date, is characterized by the fact that the machine is equipped with systems and tools that can understand the characteristics of the surrounding environment and improve its learning. In this case, it tends to provide the machine with external support elements in order to make learning more effective. An example is an unmanned car, in which thanks to a complex system of support sensors are able to travel through city streets and not, recognise any obstacles, follow the road signs and much more.
Machine learning algorithms, application fields
The fields of application of machine learning algorithms are multiple. For example, in the energy sector, machine learning algorithms can be used to find new energy resources, to analyze minerals in the soil, predict a failure in sensors used in refineries, to make oil distribution more streamlined and efficient.
Or in the field of sales and marketing, machine learning can be used to store, analyze and use consumer purchase data in order to personalize the shopping experience and target marketing campaigns, leveraging the interest revealed by each user and purchases already made.
Finally, banks and other companies in the financial industry also use machine learning technologies for two main purposes: identifying important information in data and preventing fraud. Information can identify investment opportunities and help investors know when to act. Instead, data mining can identify customers with highly risky profiles or use cyber surveillance to report fraud alerts.
Machine learning algorithms, iteration
The most important aspect of machine learning is repetition, the more models are exposed to the data, the more they are able to adapt independently. Thanks to new processing technologies, today’s machine learning is not the machine learning of the past. This science is not new but it is gaining new momentum. And although many machine learning algorithms have been around for a long time, the ability to apply complex mathematical calculations to big data is a more recent development that achieves increasingly satisfactory results.
