Machine learning and artificial intelligence / Ameet V Joshi.
Joshi, Ameet V.
2020
Q325.5
Available Online

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Title Machine learning and artificial intelligence / Ameet V Joshi.
Author Joshi, Ameet V.
ISBN 3030266222
9783030266226 (electronic book)
9783030266219
Published Cham : Springer, 2020.
Language English
Description 1 online resource (xxii, 261 pages) : illustrations
Other Standard Identifiers 10.1007/978-3-030-26
Call Number Q325.5
Dewey Decimal Classification 006.3/1
621.382
Summary 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.
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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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