Publication year: 2022
ISBN: 978-981-16-8193-6
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Approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. Trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
Subject: Machine Learning, Data Science, Models of Computation, Data Mining, Knowledge Discovery, Modelling, Artificial Intelligence, Deep Learning, Optimization, Data Analysis, Signal Processing, Statistical Learning Theory, Applied Statistics