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Machine Learning

In: Solving Optimization Problems with the Heuristic Kalman Algorithm

Author

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  • Rosario Toscano

    (École Nationale d’Ingénieurs de Saint-Etienne)

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).

Suggested Citation

  • Rosario Toscano, 2024. "Machine Learning," Springer Optimization and Its Applications, in: Solving Optimization Problems with the Heuristic Kalman Algorithm, chapter 0, pages 203-238, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-52459-2_7
    DOI: 10.1007/978-3-031-52459-2_7
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