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Applied Machine Learning by David Forsyth: New
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N.º de artículo de eBay:285546951849
Última actualización el 16 dic 2023 12:52:17 H.EspVer todas las actualizacionesVer todas las actualizaciones
Características del artículo
- Estado
- Book Title
- Applied Machine Learning
- Publication Date
- 2019-07-25
- Pages
- 494
- ISBN
- 9783030181130
Acerca de este producto
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3030181138
ISBN-13
9783030181130
eBay Product ID (ePID)
6038587803
Product Key Features
Number of Pages
Xxi, 494 Pages
Language
English
Publication Name
Applied Machine Learning
Subject
Intelligence (Ai) & Semantics, Probability & Statistics / General, Computer Science
Publication Year
2019
Type
Textbook
Subject Area
Mathematics, Computers
Format
Hardcover
Dimensions
Item Weight
55.5 Oz
Item Length
10 in
Item Width
7 in
Additional Product Features
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
1. Learning to Classify.- 2. SVM's and Random Forests.- 3. A Little Learning Theory.- 4. High-dimensional Data.- 5. Principal Component Analysis.- 6. Low Rank Approximations.- 7. Canonical Correlation Analysis.- 8. Clustering.- 9. Clustering using Probability Models.- 10. Regression.- 11. Regression: Choosing and Managing Models.- 12. Boosting.- 13. Hidden Markov Models.- 14. Learning Sequence Models Discriminatively.- 15. Mean Field Inference.- 16. Simple Neural Networks.- 17. Simple Image ClassiFiers.- 18. Classifying Images and Detecting Objects.- 19. Small Codes for Big Signals.- Index.
Synopsis
Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code. A companion to the author's Probability and Statistics for Computer Science , this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of: - classification using standard machinery (naive bayes; nearest neighbor; SVM)- clustering and vector quantization (largely as in PSCS)- PCA (largely as in PSCS)- variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)- linear regression (largely as in PSCS)- generalized linear models including logistic regression- model selection with Lasso, elasticnet- robustness and m-estimators- Markov chains and HMM's (largely as in PSCS)- EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they've been through that, the next one is easy- simple graphical models (in the variational inference section)- classification with neural networks, with a particular emphasis onimage classification- autoencoding with neural networks- structure learning, Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code. A companion to the author's Probability and Statistics for Computer Science , this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of: * classification using standard machinery (naive bayes; nearest neighbor; SVM) * clustering and vector quantization (largely as in PSCS) * PCA (largely as in PSCS) * variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis) * linear regression (largely as in PSCS) * generalized linear models including logistic regression * model selection with Lasso, elasticnet * robustness and m-estimators * Markov chains and HMM's (largely as in PSCS) * EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they've been through that, the next one is easy * simple graphical models (in the variational inference section) * classification with neural networks, with a particular emphasis on image classification * autoencoding with neural networks * structure learning, Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but aren't necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one's own code. A companion to the author's Probability and Statistics for Computer Science , this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of: - classification using standard machinery (naive bayes; nearest neighbor; SVM)- clustering and vector quantization (largely as in PSCS)- PCA (largely as in PSCS)- variants of PCA (NIPALS; latent semantic analysis; canonical correlation analysis)- linear regression (largely as in PSCS)- generalized linear models including logistic regression- model selection with Lasso, elasticnet- robustness and m-estimators- Markov chains and HMM's (largely as in PSCS)- EM in fairly gory detail; long experience teaching this suggests one detailed example is required, which students hate; but once they've been through that, the next one is easy- simple graphical models (in the variational inference section)- classification with neural networks, with a particular emphasis onimage classification- autoencoding with neural networks- structure learning
LC Classification Number
Q334-342
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- e***n (390)- Votos emitidos por el comprador.Mes pasadoCompra verificadaGreat transaction, exactly as described, packed well, and promptly shipped on August 6th. Unfortunately the U.S. Postal Service took 23 calendar days to deliver the book. It was shipped from Pennsylvania, to Atlanta, past Alabama to Texas, enjoyed several days in Texas, then to Minneapolis, Jacksonville, Florida, back to Atlanta, finally to Birmingham, and Huntsville. The seller was very responsive and I decided it was interesting to see if/how the book would arrive. Thanks, Joe
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AlibrisBooks
98,8% de votos positivos•2,0 millones artículos vendidos
Registrado como vendedor profesional
Votos de vendedor (526.917)
- e***n (390)- Votos emitidos por el comprador.Mes pasadoCompra verificadaGreat transaction, exactly as described, packed well, and promptly shipped on August 6th. Unfortunately the U.S. Postal Service took 23 calendar days to deliver the book. It was shipped from Pennsylvania, to Atlanta, past Alabama to Texas, enjoyed several days in Texas, then to Minneapolis, Jacksonville, Florida, back to Atlanta, finally to Birmingham, and Huntsville. The seller was very responsive and I decided it was interesting to see if/how the book would arrive. Thanks, Joe
- m***m (2347)- Votos emitidos por el comprador.Últimos 6 mesesCompra verificadaI’m thrilled with my recent purchase . The website was user-friendly, and the product descriptions were accurate. Customer service was prompt and helpful, answering all my questions. My order arrived quickly, well-packaged, and the product exceeded my expectations in quality. I’m impressed with the attention to detail and the overall experience. I’ll definitely shop here again and highly recommend from this seller to others. Thank you for a fantastic experience!Tobin's Spirit Guide: Official Ghostbusters Edition by Erik Burnham: Used (#404302598631)
- _***b (57)- Votos emitidos por el comprador.Mes pasadoCompra verificadaI gave 5 stars on shipping because i sent 2 separate emails + they responded with helpful info, even though it arrived late. This was a great value with free shipping + the condition is very good, better than advertised 🙂! The overall quality and appearance is excellent! I highly recommend this seller and give them 👍👍👍👍