Machine Learning Engineers by Simeone Osvaldo (17 results)

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Condition: New. Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering problems. With reproducible examples using Matlab, and lecture slides and solutions for in.

Hardcover. Condition: Brand New. 450 pages. 10.00x8.00x1.31 inches. In Stock.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - All stars are known to power strong stellar winds at the end of their lives, expelling stellar material that is recycled as building blocks of new planets and life. IAU S366 provides an overview of state-of-the-art observational, theoretical and numerical studies on the origin of winds in evolved stars.

Buch. Condition: Neu. Machine Learning for Engineers | Osvaldo Simeone | Buch | Gebunden | Englisch | 2022 | Cambridge University Press | EAN 9781316512821 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Hardcover. Condition: new. Hardcover. 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. Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering problems. With reproducible examples using Matlab, and lecture slides and solutions for instructors, this is the ideal introduction for engineering students of all disciplines. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condition: Brand New. 450 pages. 10.00x8.00x1.31 inches. In Stock. This item is printed on demand.

Hardcover. Condition: new. Hardcover. 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. Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering problems. With reproducible examples using Matlab, and lecture slides and solutions for instructors, this is the ideal introduction for engineering students of all disciplines. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condition: new. Hardcover. 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. Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering problems. With reproducible examples using Matlab, and lecture slides and solutions for instructors, this is the ideal introduction for engineering students of all disciplines. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.