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Zelfrijdende auto's, gezichtsdetectie software en spraak gestuurde speakers zijn allemaal gebouwd op machine learning technologieën en frameworks en dit is pas de eerste golf. Vandaag vertellen we je hoe je onderdeel kan zijn van deze nieuwe revolutie in combinatie met Java.

Supervised learning vs. unsupervised learning

Supervised learning and unsupervised learning are the most popular approaches to machine learning. Both require feeding the machine a massive number of data records to correlate and learn from. Such collected data records are commonly known as a feature vectors. In the case of an individual house, a feature vector might consist of features such as overall house size, number of rooms, and the age of the house.

In supervised learning, a machine learning algorithm is trained to correctly respond to questions related to feature vectors. To train an algorithm, the machine is fed a set of feature vectors and an associated label. Labels are typically provided by a human annotator, and represent the right "answer" to a given question. The learning algorithm analyzes feature vectors and their correct labels to find internal structures and relationships between them. Thus, the machine learns to correctly respond to queries.

As an example, an intelligent real estate application might be trained with feature vectors including the size, number of rooms, and respective age for a range of houses. A human labeler would label each house with the correct house price based on these factors. By analyzing that data, the real estate application would be trained to answer the question: "How much money could I get for this house?"

After the training process is over, new input data will not be labeled. The machine will be able to correctly respond to queries, even for unseen, unlabeled feature vectors.

In unsupervised learning, the algorithm is programmed to predict answers without human labeling, or even questions. Rather than predetermine labels or what the results should be, unsupervised learning harnesses massive data sets and processing power to discover previously unknown correlations. In consumer product marketing, for instance, unsupervised learning could be used to identify hidden relationships or consumer grouping, eventually leading to new or improved marketing strategies.

This article focuses on supervised machine learning, which is the most common approach to machine learning today.

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