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Machine learning and artificial intelligence / Ameet V. Joshi.

Joshi, Ameet V., author.
2023
Q325.5 .J67 2023
Available Online
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Details

Title
Machine learning and artificial intelligence / Ameet V. Joshi.
Author
Joshi, Ameet V., author.
Edition
Second edition.
ISBN
9783031122828 electronic book
3031122828 electronic book
9783031122811
303112281X
DOI
https://doi.org/10.1007/978-3-031-12282-8
Published
Cham, Switzerland : Springer, 2023.
Language
English
Description
1 online resource (1 volume) : illustrations (black and white, and colour).
Item Number
10.1007/978-3-031-12282-8 doi
Call Number
Q325.5 .J67 2023
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references (pages 265-268 ) and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Description based on print version record.
Available in Other Form
Machine learning and artificial intelligence.
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Online Access
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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
Amazons Machine Learning Toolkit: Sagemaker
Conclusion.

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