Machine learning is the future
What is machine learning? In simple terms, this is one of the main subsections of the science of artificial intelligence, which includes a combination of such sciences as mathematical and discrete analysis, mathematical statistics, and where without optimization, because the operation of machines needs to be optimized somehow. There are such types of learning: by example, transferring expert knowledge to the machine. Let’s dwell on them in more detail.
Learning by example
An intelligent machine is given a database of positive and negative examples that are tied to some unknown regularity, then rules are created according to which the machine is divided into “bad" and “good”.
Let’s say the separation has passed, but how to check the correctness? For this there is an examination sample of examples. Information is entered in the form of a set of feature descriptions, but the values of some objects may not always be completely filled. In this case, an algorithm is developed that calculates the value of the object, according to other features. The question arises, “What if the value was missed?” Then they are filled with predictive functions. Still signs can be restored, for example, if the sign indicates quantity, the regression recovery method is used, and if it is qualitative, the classification method is used.
It provides for the formalization of expert knowledge, with the transfer to the EOM as a knowledge base. As a rule, this method is considered to be the domain of an expert system, but what is an expert system? ES is a kind of computer system that can partially replace a specialist to solve some problematic task.
Machine learning. Ways
Since machine learning is a combination of several sciences, the learning methods will be combined, but still the basis is the science of neural networks. There are such methods: Learning with the help of a teacher – a pair of “situation, method of solution” is given. Includes:
Learning without the help of a teacher – only the “situation” is set, and the system itself must group objects into clusters, using information about pairwise similarities of objects. Includes:
A special teaching method – a pair of "situation, decision" is given. Only the genetic algorithm is included. There are also such learning methods as active, multitasking, learning with many options, and so on. It’s all designed to be as flexible as possible.
Tasks to be solved
Machine learning can solve problems related to:
- Classification. Thanks to the feature-decision pair, the system classifies the data;
- single class classification. Defines some similarities of objects and classifies them as classes.
Injected data Machine learning uses the following types of data:
- Description of the features of the object;
- Signal or time series;
- A number of digital images.
Where is machine learning used?
Due to its effectiveness, it can be used in various fields, but most often machine learning is effective in medicine, trading, as these are the two most dynamic areas where you need to constantly work with new volumes of data, learn how to compile a database in real time, and an ordinary person unable to keep track of everything.
Machine learning in medicine
Why medicine? Yes, because for a more thorough diagnosis and drawing up the correct course of treatment, it is necessary to take into account many different factors, for example, the level of certain hormones, sugar, leukocytes, erythrocytes, hemoglobin, and depending on the decrease or increase, predict the disease, determine pathological changes in the bud. Therefore, many years ago, specifically for medical purposes, an algorithm of actions was developed in a given situation, which allows you to act correctly in critical situations.
Machine learning in medicine can prevent many deaths. How it works? The machine with which it examines the patient, studies the clinical situation, remembers its signs and develops a solution. It is worth noting that machine learning in medicine makes it possible to study the disease that has arisen and, based on the knowledge gained, to develop effective methods of treatment. Symptoms of diseases are entered into the database, then if a relapse occurs, data on the disease is extracted and processed.
Machine learning in trading
Trading is one of the most developing sectors of earnings with many nuances. Machine learning is actively used by traders to effectively train their systems. How does machine learning work in trading? For example, there is a certain platform for trading, let’s say Steam. The system studies prices, rates of decline and displays a forecast – whether the price will fall or increase, and also evaluates risk factors and already displays the most effective price.
Also in the foreign exchange market – the terms are studied in detail during which the currency fell, the growth of the currency increased and the forecast is also displayed. The system then remembers the results and sets a future price based on them.
Machine learning is a sure step into the not-too-distant future. Machine learning systems, ways to increase their efficiency at times. For example, in economics, the system studies various economic features in the market, according to algorithms, and predicts the future. Yes, and the errors are minimal, compared with a person.