Machine Learning: An Applied Mathematics Introduction by Wilmott

Machine Learning: An Applied Mathematics Introduction by Wilmott

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Machine Learning: An Applied Mathematics Introduction by Wilmott

Machine learning has become an increasingly important field in the world of technology and data science. In his book “Machine Learning: An Applied Mathematics Introduction,” author Paul Wilmott explores the mathematical foundations of machine learning and its practical applications.

Wilmott begins by introducing the basic concepts of machine learning, such as supervised and unsupervised learning, regression, and classification. He then delves into the mathematical principles that underlie these techniques, including linear algebra, calculus, and probability theory.

Through a series of practical examples and exercises, Wilmott demonstrates how these mathematical concepts can be applied to real-world machine learning problems. From predicting stock prices to identifying spam emails, machine learning has a wide range of applications that can benefit from a solid understanding of mathematics.

Whether you are a student looking to deepen your understanding of machine learning or a professional seeking to improve your skills, “Machine Learning: An Applied Mathematics Introduction” offers a comprehensive and accessible introduction to the mathematical foundations of this exciting field.
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