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Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence

Wydawnictwo Springer Nature Customer Service Center GmbH
Data wydania 01/10/2019
Wydanie Pierwsze
Liczba stron 261
Forma publikacji książka w twardej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
Język angielski
ISBN 9783030266219
Kategorie Inżynieria komunikacyjna i telekomunikacyjna, Sztuczna inteligencja, Machine learning
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Opis książki

This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (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 forth 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. 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. Presents a full reference to artificial intelligence and machine learning techniques - in theory and application; Provides a guide to AI and ML with minimal use of mathematics to make the topics more intuitive and accessible; Connects all ML and AI techniques to applications and introduces implementations.

Machine Learning and Artificial Intelligence

Spis treści

Introduction.- Part I Introduction to AI and ML.- Essential concepts in AL and ML.- Part II Techniques for Static Machine Learning Models.- Perceptron and Neural Networks.- Decision Trees.- Advanced Decision Trees.- Support Vector Machines.- Probabilistic Models.- Deep Learning.- Part III Techniques for Dynamic Machine Learning Models.- Autoregressive and Moving Average Models.- Hidden Markov Models and Conditional Random Fields.- Recurrent Neural Networks.- Part IV Applications.- Classification Regression.- Ranking.- Clustering.- Recommendations.- Next Best Actions.- Designing ML Pipelines.- Using ML Libraries.- Azure Machine Learning Studio.- Conclusions.

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