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- W4320060287 abstract "This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines." @default.
- W4320060287 created "2023-02-12" @default.
- W4320060287 date "2022-11-03" @default.
- W4320060287 modified "2023-09-27" @default.
- W4320060287 title "Optimization for Machine Learning" @default.
- W4320060287 doi "https://doi.org/10.1017/9781009072205.008" @default.
- W4320060287 hasPublicationYear "2022" @default.
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