机读格式显示(MARC)
- 000 02219cam a2200337 i 4500
- 008 220303t20232023enka b 001 0 eng d
- 020 __ |a 9781316512821 |q hardcover
- 020 __ |a 1316512827 |q hardcover
- 040 __ |a DLC |b eng |e rda |c DLC |d OCLCF |d CDX |d YDX
- 050 00 |a TA345 |b .S5724 2023
- 082 00 |a 620.00285 |2 23
- 099 __ |a CAL 022023038434
- 100 1_ |a Simeone, Osvaldo, |e author.
- 245 10 |a Machine learning for engineers / |c Osvaldo Simeone, King's College London.
- 264 _1 |a Cambridge, United Kingdom ; |a New York, NY : |b Cambridge University Press, |c 2023.
- 300 __ |a xxii, 578 pages : |b illustrations ; |c 27 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 504 __ |a Includes bibliographical references and index.
- 520 __ |a "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 : 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, and 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"-- |c Provided by publisher.
- 650 _0 |a Engineering |x Data processing.
- 650 _0 |a Machine learning.