Linear Algebra, Data Science, and ML by Jeff Calder (.PDF)
File Size: 51.6 MB
Linear Algebra, Data Science, and Machine Learning by Jeff Calder, Peter J. Olver
Requirements: .PDF reader, 51.6 MB
Overview: This text provides a mathematically rigorous introduction to modern methods of Machine Learning (ML) and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary Machine Learning and data analysis. To introduce the reader to a broad range of Machine Learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises.
Genre: Non-Fiction > Tech & Devices
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