Produkt
KlappentextMachine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
ZusammenfassungThis practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors. A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Details
ISBN/GTIN978-0-521-51814-7
ProduktartBuch
EinbandartGebunden
Erscheinungsjahr2019
Erscheinungsdatum15.02.2019
Seiten735 Seiten
SpracheEnglisch
Gewicht1710 g
Illustrationen287 b/w illus. 1 table 260 exercises
Artikel-Nr.11461675
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