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020			_a9783031122828
024	7		_a10.1007/978-3-031-12282-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2023 EB
100	1		_aJoshi, Ameet V. _eautor _4http://id.loc.gov/vocabulary/relators/aut _9671602
245	1	0	_aMachine Learning and Artificial Intelligence _cby Ameet V. Joshi
250			_a2nd ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- 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.
520			_aThe 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9166090 _aAprendizaje automático
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-12282-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI