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Machine Learning and Artificial Intelligence / by Ameet V Joshi

データ種別	電子ブック
版	2nd ed. 2023.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2023
大きさ	XXI, 271 p. 129 illus., 125 illus. in color : online resource
著者標目	*Joshi, Ameet V author SpringerLink (Online service)

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URL	巻　次	配架場所	請求記号	登録番号	コメント	刷　年	状　態	利用注記	ISBN	予約	請求メモ
URL		射水-電子	007	EB0001192	Computer Scinece R0 2005-6,2022-3				9783031122828

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一般注記	Introduction -- Introduction to AI and ML -- Essential Concepts in Artificial Intelligence and Machine Learning -- Data Understanding, Representation, and Visualization -- Linear Methods -- Perceptron and Neural Networks -- Decision Trees -- Support Vector Machines -- Probabilistic Models -- Dynamic Programming and Reinforcement Learning -- Evolutionary Algorithms -- Time Series Models -- Deep Learning -- Emerging Trends in Machine Learning -- Unsupervised Learning -- Featurization -- Designing and Tuning -- Model Pipelines -- Performance Measurement -- Classification -- Regression -- Ranking -- Recommendations Systems -- Azure Machine Learning -- Open Source Machine Learning Libraries -- Amazon’s Machine Learning Toolkit: Sagemaker -- Conclusion The new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs HTTP:URL=https://doi.org/10.1007/978-3-031-12282-8
件　名	LCSH:Telecommunication LCSH:Machine learning LCSH:Artificial intelligence LCSH:Computational intelligence FREE:Communications Engineering, Networks FREE:Machine Learning FREE:Artificial Intelligence FREE:Computational Intelligence
分　類	LCC:TK5101-5105.9 DC23:621.382
書誌ID	EB00000580
ISBN	9783031122828