Machine learning is a branch of artificial intelligence, and more specifically of computer science, which deals with the study of systems and algorithms that can learn from data, synthesizing new knowledge from them.
The word learn intuitively suggests that a system based on machine learning, may, on the basis of the observation of previously processed data, improve its knowledge in order to achieve better results in the future, or provide output closer to the desired output for that particular system.
The ability of a program or a system based on machine learning to improve its performance in a particular task, thanks to past experience, is strongly linked to its ability to recognize patterns in the data. This theme, called pattern recognition, is therefore of vital importance and of increasing interest in the context of artificial intelligence; it is the basis of all machine learning techniques.
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The training of a machine learning system can be done in different ways:
Supervised learning Unsupervised learning
Supervised learning
Supervised learning is the most common form of machine learning. With supervised learning, a set of examples, the training set, is submitted as input to the system during the training phase, where each example is labeled with the respective desired output value. For example, let's consider a classification problem, where the system must attribute some experimental observations in one of the N different classes already known. In this problem, the training set is presented as a sequence of pairs of the type {(X1, Y1), ....., (Xn, Yn)} where Xi are the input vectors (feature vectors) and Yi represents the desired class for the corresponding input vector. Most supervised learning algorithms share one characteristic: the training is performed by the minimization of a particular loss function (cost function), which represents the output error with respect to the desired output system.
The cost function most used for this type of training calculates the standard deviation between the desired output and the one supplied by the system. After training, the accuracy of the model is measured on a set of disjointed examples from the training set, the so-called validation set.
In this phase the model's generalization capability is then verified: we will test if the output is
correct for an unused input during the training phase.
Unsupervised learning
In unsupervised learning, the training examples provided by the system are not labeled with the related belonging class. The system, therefore, develops and organizes the data, looking for common characteristics among them, and changing them based on their internal knowledge.
Unsupervised learning algorithms are particularly used in clustering problems, in which a number of input examples are present, you do not know the class a priori, and you do not even know what the possible classes are, or how numerous they are. This is a clear case when you cannot use supervised learning, because you do not know a priori the number of classes.
Deep learning
Deep learning techniques represent a remarkable step forward taken by machine learning in recent decades, having provided results never seen before in many applications, such as image and speech recognition or Natural Language Processing (NLP). There are several reasons that led to deep learning being developed and placed at the center of the field of machine learning only in recent decades. One reason, perhaps the main one, is surely represented by progress in hardware, with the availability of new processors, such as graphics processing units (GPUs), which have greatly reduced the time needed for training networks, lowering them by a factor of 10 or 20. Another reason is certainly the ever more numerous datasets on which to train a system, needed to train architectures of a certain depth and with a high dimensionality for the input data.Deep learning is based on the way the human brain processes information and learns, responding to external stimuli. It consists in a machine learning model at several levels of representation in which the deeper levels take as input the outputs of the previous levels, transforming them and always abstracting more. Each level corresponds in this hypothetical model to a different area of the cerebral cortex: when the brain receives images, it processes them through various stages such as edge detection and form perception, that is, from a primitive representation level to the most complex. For example, in an image classification problem, each block gradually extracts the features, at various levels of abstraction, inputting of data already processed, by means of filtering operations.Deep learning is based on the way the human brain processes information and learns, responding to external stimuli. It consists in a machine learning model at several levels of representation in which the deeper levels take as input the outputs of the previous levels, transforming them and always abstracting more. Each level corresponds in this hypothetical model to a different area of the cerebral cortex: when the brain receives images, it processes them through various stages such as edge detection and form perception, that is, from a primitive representation level to the most complex. For example, in an image classification problem, each block gradually extracts the features, at various levels of abstraction, inputting of data already processed, by means of filtering operations.
