Machine Learning

Rosario Toscano
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Rosario Toscano: École Nationale d’Ingénieurs de Saint-Etienne

Chapter Chapter 7 in Solving Optimization Problems with the Heuristic Kalman Algorithm, 2024, pp 203-238 from Springer

Abstract: Abstract Machine learning is an area of artificial intelligence that aims to develop systems that can learn and improve from data. The central concept of machine learning is based on the idea of using algorithms to analyze and interpret sets of data, in order to detect patterns, relationships, or trends. There are different types of machine learning algorithms, including supervised learning and unsupervised learning. In supervised learning, models are trained on labeled data, which means data for which the desired response is known. The algorithm learns to associate data features with their corresponding labels, enabling it to make predictions about new, unlabeled data. In contrast, in unsupervised learning, models are used to discover hidden structures or patterns in data without prior labels. These algorithms can group the data based on similarities (e.g., clustering) or reduce the data to smaller dimensions to facilitate understanding (e.g., principal component analysis).

Date: 2024
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DOI: 10.1007/978-3-031-52459-2_7

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