Nowadays we hear the word “Machine Learning” more and more often. In this article we will analyze this unprecedented word so that everyone understands it. To begin with, learning is associated with 2 entities. The first one is the ability to obtain knowledge when you interact with your surroundings and the second one is about the improvement of an action through iteration.
Machine learning is the ability to understand the environment in an abstract way or in other words machine learning means modeling. This modeling is called inductive learning. In addition, the field of machine learning is directly connected to what we called patterns. When we talk about patterns we talk about associations, experiences, and new structures. At this point it would be wise to analyze the types of machine learning. According to the nature of the problem there are 2 types. The first one is called supervised learning and the second one is called unsupervised learning. In accordance with the supervised learning, the system should learn a concept or function from a set of data, which is a description of a model. In unsupervised learning, the system is forced to discover associations or a set of data, creating patterns without knowing if there any or not.
Moreover, it critical to mention that plenty of algorithms have been implemented for the needs of machine learning. One of them is the Candidate Elimination Algorithm which restricts the field of research by implementing generalizations and specializations on some initial speculations based on education data. What is more, this algorithm contains 2 set which are called G and S and their operation is the description of research field. Another significant machine learning algorithm is the one of ID3. This algorithm is retroactive, and its logic goes as follows:
- Find the independent variable which if used as a criterion for separating training data will lead to nodes as different as possible from the dependent variable
- Implement the separation
- Repeat the procedure for each of the resulting nodes until further separation is possible
In other words, ID3 algorithm constructs a tree in a greedy way from top to bottom by selecting the most appropriate attribute for root control.
After a brief analysis of what machine learning entails, it is time to talk about another field which is directly connected to it, the field of neural networks. Simply speaking, neural networks provide a handy way to learn numerical and vector functions of some continuous or discrete quantities. These networks are used for both interpolation (linear and non-linear), as well as for classification/ categorization. They have the great advantage of tolerating the noise education data, but they fail to explain qualitatively the knowledge they model. Additionally, neural networks rely on the Statistical Learning Theory and are one of the most famous methods for interpolation and classification with plenty of applications such as handwriting recognition, text categorization and gene expression data.
In conclusion, it is safe to say that both machine learning and neural networks have been established in a lot of disciplines and have plenty of applications with more to come. If we put it differently we can say that these new technologies are going to play a significant role in the foreseeable future (if they haven’t already done it) since more and more technologies are based on them.