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One can define machine learning (ML) as programs that learn and improve with the use of the experience at some task using a measure of the performance. We can classify ML techniques basing on the desired outcome of the algorithm. There are, in the classic taxonomy, three main types of learning: (1) supervised learning , where an expert provides feedback in the learning process, (2) unsupervised learning , where there is no teacher or expert when the learning process is running, and (3) reinforcement learning , where the program learns interacting with the environment. Of all these, we will talk about the first one. Supervised learning is a ML method for extracting a model from training data. These data consist of a set (called examples ) of input attributes (sometimes called features ) and the desired output. As commented before, the main characteristic of supervised learning is that the program needs an expert or teacher that provides feedback in the learning process. Typically, the

Over the past decades the study of machine learning (ML) has grown from the efforts of engineers exploring whether computers could learn to perform some tricky actions to a broad discipline. The purpose of this blog is to give my personal view and thoughts on this wonderful subject. Feel free to comment whatever you wish.