Practical Machine Learning Tools and Techniques
E-Book, Englisch, 688 Seiten
ISBN: 978-0-443-15889-6
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark
Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today's techniques coupled with the methods at the leading edge of contemporary research - Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects - Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Features in-depth information on deep learning and probabilistic models - Covers performance improvement techniques, including input preprocessing and combining output from different methods - Provides an appendix introducing the WEKA machine learning workbench and links to algorithm implementations in the software - Includes all-new exercises for each chapter
Dr. James (Jimmy) Foulds is an assistant professor in the Department of Information Systems at the University of Maryland, Baltimore County. Previously, he was a postdoctoral scholar at the University of California, San Diego under the Data Science Postdoctoral Fellowship program, co-sponsored by ITA, Calit2, the Qualcomm Institute, CSE and ECE. Prior to that he was a postdoctoral scholar in Lise Getoor's LINQS research group at UCSC, and he graduated from Padhraic Smyth's DataLab group at UCI. Dr. Foulds' research interests are broadly in socially conscious machine learning and artificial intelligence. His work aims to improve AI's role in society regarding fairness and privacy, and to promote the practice of computational social science, using probabilistic models and Bayesian inference.
Witten / Foulds / Frank
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Dr. James (Jimmy) Foulds is an assistant professor in the Department of Information Systems at the University of Maryland, Baltimore County. Previously, he was a postdoctoral scholar at the University of California, San Diego under the Data Science Postdoctoral Fellowship program, co-sponsored by ITA, Calit2, the Qualcomm Institute, CSE and ECE. Prior to that he was a postdoctoral scholar in Lise Getoor's LINQS research group at UCSC, and he graduated from Padhraic Smyth's DataLab group at UCI. Dr. Foulds' research interests are broadly in socially conscious machine learning and artificial intelligence. His work aims to improve AI's role in society regarding fairness and privacy, and to promote the practice of computational social science, using probabilistic models and Bayesian inference.
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