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Books on the topic 'Search engines; machine learning'

Author: Grafiati

Published: 3 June 2025

Last updated: 13 July 2025

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1

Pillay, Nelishia, and Rong Qu, eds. Automated Design of Machine Learning and Search Algorithms. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72069-8.

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2

Pesch, Erwin. Learning in automated manufacturing: A local search approach. Physica-Verlag, 1994.

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3

Knoblock, Craig A. Generating abstraction hierarchies: An automated approach to reducing search in planning. Kluwer Academic Publisher, 1993.

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4

Hearst, Marti. Search user interfaces. Cambridge University Press, 2009.

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5

Hearst, Marti. Search user interfaces. Cambridge University Press, 2009.

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6

Ackermann, Ernest C. Learning to use the Internet and World Wide Web with revitalized URLs. Franklin, Beedle & Associates, 2004.

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7

Lim, Edward H. Y. Knowledge Seeker - Ontology Modelling for Information Search and Management: A Compendium. Springer-Verlag Berlin Heidelberg, 2011.

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8

Inc, ebrary, ed. Learning the Yahoo! user interface library: Get started and get to grips with the YUI JavaScript development library! Packt, 2008.

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9

1947-, Miller William, and Pellen Rita M, eds. Libraries and Google. Haworth Information Press, 2005.

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10

Recommendation Engines. MIT Press, 2020.

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11

Wiley. Learning Search Engine Optimization. Wiley & Sons, Incorporated, John, 2013.

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12

Shroff, Gautam. The Intelligent Web. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199646715.001.0001.

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As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.

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13

Friesen, Matt Bailey, and Video2brain Staff. Learning Search Engine Optimization: A Video Introduction. Wiley & Sons, Incorporated, John, 2013.

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14

video2brain. Search Engine Optimization Essential Learning Kit. Wiley & Sons, Incorporated, John, 2013.

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15

Large-Scale Machine Learning for Classification and Search. [publisher not identified], 2012.

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16

Pillay, Nelishia, and Rong Qu. Automated Design of Machine Learning and Search Algorithms. Springer International Publishing AG, 2021.

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17

Pillay, Nelishia, and Rong Qu. Automated Design of Machine Learning and Search Algorithms. Springer International Publishing AG, 2022.

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18

Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Pub. Co., 1989.

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19

Banks, Allister, and Charles S. Edge. Learning IOS Security. Packt Publishing, Limited, 2015.

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20

Banks, Allister, and Charles S. Edge. Learning iOS Security. Packt Publishing, 2015.

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21

Breyer, Avery. Turn your computer into a money machine. 2015.

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22

Collier, Rich, and Bahaaldine Azarmi. Machine Learning with the Elastic Stack: Expert Techniques to Integrate Machine Learning with Distributed Search and Analytics. Packt Publishing, Limited, 2019.

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23

Building Recommendation Engines. Packt Publishing, 2016.

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24

Glymour, Clark, Richard Scheines, and Peter Spirtes. Causation, Prediction, and Search, Second Edition (Adaptive Computation and Machine Learning). 2nd ed. The MIT Press, 2001.

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25

Andhavarapu, Abhishek. Learning Elasticsearch: Structured and unstructured data using distributed real-time search and analytics. Packt Publishing - ebooks Account, 2017.

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26

Downing, Keith L. Intelligence Emerging - Adaptivity and Search in Evolving Neural Systems. MIT Press, 2015.

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27

Automated Machine Learning: Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms. Packt Publishing, 2021.

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28

Search user interfaces. Cambridge University Press, 2009.

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29

Greenwald, Amy. The Search for Equilibrium in Markov Games (Synthesis Lectures on Artificial Intelligence and Machine Learning). Morgan & Claypool Publishers, 2007.

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30

Murty, M. N., and Anirban Biswas. Centrality and Diversity in Search: Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition. Springer, 2019.

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31

Downing, Keith L. Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems. The MIT Press, 2015.

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32

Downing, Keith L. Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems. MIT Press, 2015.

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33

Downing, Keith L. Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems. MIT Press, 2015.

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34

Downing, Keith L. Intelligence Emerging: Adaptivity and Search in Evolving Neural Systems. MIT Press, 2015.

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35

Patel, Devangini. Hands-On Artificial Intelligence for Search: Building intelligent applications and perform enterprise searches. Packt Publishing, 2018.

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36

Ai and Machine Learning : the Ultimate Guide to Business and Marketing: Knowledge Representation + Solution Search Methods. Independently Published, 2022.

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37

Lee, Raymond S. T., Edward H. Y. Lim, and James N. K. Liu. Knowledge Seeker - Ontology Modelling for Information Search and Management: A Compendium. Springer, 2013.

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38

Fun Activity Book for Kids, Activity Books for 7-12 Years Old- Learning Activity Including Time Machine Project, Word Search, Spot the Difference, Scrambled Words, Sudoku, Number Puzzle and Colouring: Fun and Educational Activity Book. Independently Published, 2022.

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39

Mohan, Vineeth. Elasticsearch Blueprints: A Practical Project-Based Guide to Generating Compelling Search Solutions Using the Dynamic and Powerful Features of Elasticsearch. Packt Publishing, Limited, 2015.

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40

Elasticsearch Blueprints. Packt Publishing, Limited, 2015.

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41

William, Miller, and Rita M. Pellen. Libraries and Google. Taylor & Francis Group, 2014.

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42

(Editor), William Miller, and Rita M. Pellen (Editor), eds. Libraries And Google (Internet Reference Services Quarterly) (Internet Reference Services Quarterly). Haworth Information Press, 2006.

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43

William, Miller, and Rita M. Pellen. Libraries and Google. Taylor & Francis Group, 2014.

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44

(Editor), William Miller, and Rita M. Pellen (Editor), eds. Libraries And Google (Internet Reference Services Quarterly) (Internet Reference Services Quarterly). Haworth Information Press, 2006.

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45

III, Miller William. Libraries and Google. Routledge, 2014.

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46

Hill, Kristy. Teaching Elementary Students Real-Life Inquiry Skills. ABC-CLIO, LLC, 2019. http://dx.doi.org/10.5040/9798216022947.

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Fake news and misinformation is everywhere. Learn how to teach elementary students to locate reliable information, evaluate sources, and develop their writing skills in the classroom and in the library. Empower students to find and evaluate information with this practical guide to supporting classroom writing and research instruction. You'll learn ways to teach students to evaluate information for accuracy and to collect information from credible sources such as library journals. Additionally, you'll learn how to incorporate writing into your makerspace, encourage curiosity through the inquiry process, and help students to find their voice. Along the way, you'll discover how to support various writing genres including technical writing and the research project and how to teach prewriting for digital media such as websites, blogs, and social media. Lesson plans, which can be adapted from year to year as a part of the classroom and library curriculum, explain how students can use databases, search engines, books, and expert testimony to gather information. Also included are student samples and hands-on activities that will get students excited about learning. Provides guidelines elementary students can use to evaluate resources for accuracy and credibility Explains how to teach students not only where to look for information but also how to gather and use that information Offers lesson plans that build research and note-taking skills Teaches inquiry as a mode of learning

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47

Mohan Kumar, Dr S. Artificial Intelligence: Foundations, Applications, and the Generative Future. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2025. https://doi.org/10.47716/978-93-92090-63-9.

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Artificial Intelligence (AI) is no longer confined to laboratories and theoretical models; it is now a driving force behind the digital transformation of society. Artificial Intelligence: Foundations, Applications, and the Generative Future provides an in-depth journey through AI’s essential principles, its wide-ranging applications, and the revolutionary impact of generative technologies. The book methodically builds knowledge from classical AI concepts like intelligent agents, search strategies, and knowledge representation, to advanced learning models including neural networks and machine learning algorithms. It culminates in an exploration of Generative AI—highlighting its transformative role in Industry 5.0, autonomous systems, and creative industries. Designed for both academic and professional audiences, this work offers not just understanding, but vision—preparing readers to navigate and shape the next frontier of AI evolution. Keywords: Artificial Intelligence, Machine Learning, Knowledge Representation, Neural Networks, Generative AI, Deep Learning, Industry 5.0, Intelligent Systems, Future of AI, Digital Transformation

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48

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

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Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ

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49

Lichtman, Elliot. The Computer Always Wins. The MIT Press, 2025. https://doi.org/10.7551/mitpress/15544.001.0001.

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An engaging and approachable resource for beginning-to-intermediate coders eager to learn advanced ideas in computer programming. In The Computer Always Wins, Elliot Lichtman will teach you some of computer science’s most powerful concepts in a refreshingly accessible way: exploring them through word games, board games, and strategy games you already know. Learn recursion by playing tic-tac-toe, efficient search through puzzle games like sudoku and Wordle, and machine learning by way of the playground classic rock-paper-scissors. Finish the book, and you’ll come away with not only a deeper understanding of these foundational programming techniques but also a new appreciation for the amazing feats that can be accomplished using simple, readable code.

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50

Sekhon, Jasjeet. The Neyman— Rubin Model of Causal Inference and Estimation Via Matching Methods. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0011.

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This article presents a detailed discussion of the Neyman-Rubin model of causal inference. Additionally, it describes under what conditions ‘matching’ approaches can lead to valid inferences, and what kinds of compromises sometimes have to be made with respect to generalizability to ensure valid causal inferences. Moreover, the article summarizes Mill's first three canons and shows the importance of taking chance into account and comparing conditional probabilities when chance variations cannot be ignored. The significance of searching for causal mechanisms is often overestimated by political scientists and this sometimes leads to an underestimate of the importance of comparing conditional probabilities. The search for causal mechanisms is probably especially useful when working with observational data. Machine learning algorithms can be used against the matching problem.

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