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Bedankt voor het vertrouwen het afgelopen jaar! Om jou te bedanken bieden we GRATIS verzending (in België) aan op alles gedurende de hele maand januari.
- Afhalen na 1 uur in een winkel met voorraad
- In januari gratis thuislevering in België
- Ruim aanbod met 7 miljoen producten
Omschrijving
This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis.
As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
Specificaties
Betrokkenen
- Auteur(s):
- Vertaler(s):
- Uitgeverij:
Inhoud
- Aantal bladzijden:
- 532
- Taal:
- Engels
Eigenschappen
- Productcode (EAN):
- 9789819939169
- Verschijningsdatum:
- 6/12/2023
- Uitvoering:
- Hardcover
- Formaat:
- Genaaid
- Afmetingen:
- 156 mm x 234 mm
- Gewicht:
- 938 g
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