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This pedagogically designed textbook introduces the fundamental principles of machine learning in a clear and systematic way. Organized into forty-three manageable chapters, it guides students progressively through the subject, highlighting the connections between key ideas. Students develop intuition through simple examples that illustrate the often complex behaviors of machine learning models. Models and algorithms are treated mathematically, and concepts beyond linear algebra and multivariable calculus are introduced along the way.
A First Course in Machine Learning teaches the mathematics needed to deeply understand machine learning and to prepare for more advanced study, contains a carefully curated and ordered set of topics so students increasingly gain the knowledge needed to understand the subject, and includes programming exercises so the reader can experiment with the algorithms but does not rely on any particular computer language.
This pedagogically designed textbook introduces the fundamental principles of machine learning in a clear and systematic way. Organized into forty-three manageable chapters, it guides students progressively through the subject, highlighting the connections between key ideas. Students develop intu...
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À propos de l'auteur
Edmond Chow is a Professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He is a SIAM Fellow.
Edmond Chow is a Professor in the School of Computational Science and Engineering at Georgia Institute of Technology. He is a SIAM Fellow.
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