Machine learning Books

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Book SynopsisThe twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. ''Data science'' and ''machine learning'' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.Table of ContentsPart I. Classic Statistical Inference: 1. Algorithms and inference; 2. Frequentist inference; 3. Bayesian inference; 4. Fisherian inference and maximum likelihood estimation; 5. Parametric models and exponential families; Part II. Early Computer-Age Methods: 6. Empirical Bayes; 7. James–Stein estimation and ridge regression; 8. Generalized linear models and regression trees; 9. Survival analysis and the EM algorithm; 10. The jackknife and the bootstrap; 11. Bootstrap confidence intervals; 12. Cross-validation and Cp estimates of prediction error; 13. Objective Bayes inference and Markov chain Monte Carlo; 14. Statistical inference and methodology in the postwar era; Part III. Twenty-First-Century Topics: 15. Large-scale hypothesis testing and false-discovery rates; 16. Sparse modeling and the lasso; 17. Random forests and boosting; 18. Neural networks and deep learning; 19. Support-vector machines and kernel methods; 20. Inference after model selection; 21. Empirical Bayes estimation strategies; Epilogue; References; Author Index; Subject Index.

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Book SynopsisDuring the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master''s students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book coTrade Review'This book provides a unique perspective on exponential families, bringing together theory and methods into a unified whole. No other text covers the range of topics in this text. If you want to understand the 'why' as well as the `how' of exponential families, then this book should be on your bookshelf.' Larry Wasserman, Carnegie Mellon University'I am excited to see the publication of this monograph on exponential families by my friend and colleague Brad Efron. I learned some of this material during my Ph.D. studies at Stanford from the maestro himself, as well as the geometry of curved exponential families, Hoeffding's lemma, the Lindsey method, and the list goes on. They have lived with me my entire career and informed our work on GAMs and sparse GLMs. Generations of Stanford students have shared this privilege, and now generations in the future will be able to enjoy the unique Efron style.' Trevor Hastie, Stanford University'Exponential families can be magical in simplifying both theoretical and applied statistical analyses. Brad Efron's wonderful book exposes their secrets, from R. A. Fisher's early magic to Efron's own bootstrap: an essential text for understanding how data of all sizes can be approached scientifically.' Stephen Stigler, University of Chicago'This book provides an original and accessible study of statistical inference in the class of models called exponential families. The mathematical properties and flexibility of this class makes the models very useful for statistical practice – they underpin the class of generalized linear models, for example. Writing with his characteristic elegance and clarity, Efron shows how exponential families underpin, and provide insight into, many modern topics in statistical science, including bootstrap inference, empirical Bayes methodology, high-dimensional inference, analysis of survival data, missing data, and more.' Nancy Reid, University of Toronto'In this book, Brad Efron illuminates the exponential family as a practical, extendible, and crucial ingredient in all manners of data analysis, be they Bayesian, frequentist, or machine learning. He shows us how to shape, understand, and employ these distributions in both algorithms and analysis. The book is crisp, insightful, and indispensable.' David Blei, Columbia UniversityTable of Contents1. One-parameter exponential families; 2. Multiparameter exponential families; 3. Generalized linear models; 4. Curved exponential families, eb, missing data, and the em algorithm; 5. Bootstrap confidence intervals; Bibliography; Index.

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Book SynopsisThis is a practical guide to P-splines, a simple, flexible and powerful tool for smoothing. P-splines combine regression on B-splines with simple, discrete, roughness penalties. They were introduced by the authors in 1996 and have been used in many diverse applications. The regression basis makes it straightforward to handle non-normal data, like in generalized linear models. The authors demonstrate optimal smoothing, using mixed model technology and Bayesian estimation, in addition to classical tools like cross-validation and AIC, covering theory and applications with code in R. Going far beyond simple smoothing, they also show how to use P-splines for regression on signals, varying-coefficient models, quantile and expectile smoothing, and composite links for grouped data. Penalties are the crucial elements of P-splines; with proper modifications they can handle periodic and circular data as well as shape constraints. Combining penalties with tensor products of B-splines extends theseTrade Review'The title says it all. This is a practical book which shows how P-splines are used in an astonishingly wide range of settings. If you use P-splines already the book is indispensable; if you don't, then reading it will convince you it's time to start. Every example comes with an R-program available on the book's web-site, an important feature for the experienced user and novice alike.' Iain Currie, Heriot-Watt University'This book is an enlightening and at the same time extremely enjoyable read. It will serve the applied statistician who is looking for practical solutions but also the connoisseur in search of elegant concepts. The accompanying website offers reproducible code and invites to promptly enter the fascinating universe of P-splines.' Jutta Gampe, Max Planck Institute for Demographic Research'Everything you always wanted to know about P-splines, from the inventors themselves. Paul H.C. Eilers and Brian D. Marx make a compelling case for their claim that P-splines are the best practical smoother out there, providing intuition, methodology, applications, and R code that clearly demonstrate the power, flexibility, and wide applicability of this approach to smoothing.' Jeffrey Simonoff, New York University'This is the book that everyone working on smoothing models should keep handy. At last we have a manuscript that shows the real power of P-splines, their versatility, and the different perspectives you can take to use them. Chapters 1 to 3 will certainly appeal to those who want to start working in this field, and to researchers that need to deepen their knowledge of this technique. Scientists and practitioners from other areas will find chapters 4 to 8 very useful for the wide range of examples and applications. The companion package and the fact that all results (even figures) are reproducible is a real bonus. Thank you Paul and Brian for being truthful to your motto: 'show, don't tell'.' Maria Durbán, University Carlos III de MadridTable of Contents1. Introduction; 2. Bases, penalties, and likelihoods; 3. Optimal smoothing in action; 4. Multidimensional smoothing; 5. Smoothing of scale and shape; 6. Complex counts and composite links; 7. Signal regression; 8. Special subjects; A. P-splines for the impatient; B. P-splines and competitors; C. Computational details; D. Array algorithms; E. Mixed model equations; F. Standard errors in detail; G. The website.

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Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

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Book SynopsisThis book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time.

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Book SynopsisA project-based guide to the basics of deep learning.This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach.Each chapter includes a p

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Book SynopsisA textbook for developing financial risk models using optimization and simulation, with instructions for programming in various languages--

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Book SynopsisTable of Contents1. Fundamentals of Image Enhancement: Techniques and applications 2. Fundamentals of Image Reconstruction: Concepts and Challenges 3. Soft Computing based Image Reconstruction and Enhancement 4. Image Enhancement for Underwater Images 5. Image Enhancement and Reconstruction for Smart Healthcare 6. Super-resolution of medical images – to come 7. Image Enhancement Techniques Used in Remote Sensing Satellite Imagery 8. Low Contrast Image Enhancement 9. Image Dehazing – to come 10. AI for Image enhancement and reconstruction – to come 11. Enhancement and Reconstruction of Night Vision Images 12. Color Image Reconstruction and Enhancement 13. Video Enhancement and Super-resolution 14. Biometric Image Enhancement and Reconstruction 15. 3D Image Reconstruction and Enhancement 16. Deep-Learning-Based Image Reconstruction and Enhancement 17. Image Reconstruction and Enhancement for Retinal Fundus Images 18. Image Enhancement in Agriculture

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Book SynopsisMachine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics.The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additivTrade Review"Machine learning is considered promising for investment management applications, yet the associated low signal to noise ratio presents a high bar for improving on the incumbent quant asset management tooling. The book of Coqueret and Guida is a treat for those who do not want to lose sight of the machine learning forest for the trees. Whether you are an academic scholar or a finance practitioner, you will learn just what you need to rigorously investigate machine learning techniques for factor investing applications, along with plenty of useful code snippets." -Harald Lohre, Executive Director of Research at Robeco and Honorary Researcher at Lancaster University Management School"Written by two experts on quantitative finance, this book covers everything from basic materials to advanced techniques in the field of quantitative investment strategies: data processing, alpha signal generation, portfolio optimization, backtesting and performance evaluation. Concrete examples related to asset management problems illustrate each machine learning technique, such as neural network, lasso regression, autoencoder or reinforcement learning. With more than 20 coding exercises and solutions provided in Python, this publication is a must for both students, academics and professionals who are looking for an up-to-date technical exposition on quantitative asset management from basic smart beta portfolios to enhanced alpha strategies including factor investing."-Thierry Roncalli, Head of Quantitative Portfolio Strategy at Amundi Institute, Amundi Asset ManagementTable of ContentsPart 1. Introduction 1. Notations and data 2. Introduction 3. Factor investing and asset pricing anomalies 4. Data preprocessing Part 2. Common supervised algorithms 5. Penalized regressions and sparse hedging for minimum variance portfolios 6. Tree-based methods 7. Neural networks 8. Support vector machines 9. Bayesian methods Part 3. From predictions to portfolios 10. Validating and tuning 11. Ensemble models 12. Portfolio backtesting Part 4. Further important topics 13. Interpretability 14. Two key concepts: causality and non-stationarity 15. Unsupervised learning 16. Reinforcement learning Part 5. Appendix 17. Data description 18. Solutions to exercises

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Book SynopsisHuman-Robot Interaction: Safety, Standardization, and Benchmarking provides a comprehensive introduction to the new scenarios emerging where humans and robots interact in various environments and applications on a daily basis. The focus is on the current status and foreseeable implications of robot safety, approaching these issues from the standardization and benchmarking perspectives. Featuring contributions from leading experts, the book presents state-of-the-art research, and includes real-world applications and use cases. It explores the key leading sectorsârobotics, service robotics, and medical roboticsâand elaborates on the safety approaches that are being developed for effective human-robot interaction, including physical robot-human contacts, collaboration in task execution, workspace sharing, human-aware motion planning, and exploring the landscape of relevant standards and guidelines.Features Presenting aTable of Contents 1 The Role of Standardization in Technical Regulations André Pirlet 2 The intricate relationships between private standards and publicpolicymakingin the case of personal care robot. Who cares more? Eduard Fosch-Villaronga and Angelo Jr Golia 3 Standard Ontologies and HRI Sandro Rama Fiorini, Abdelghani Chibani, Tamas Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, Paulo Gonçalves, S. Veera Ragavan, Howard Li, Hirenkumar Nakawala, Stephen Balakirsky, Sofiane Bouznad, Noauel Ayari, and Yacine Amirat 4 Robot Modularity and safety for Service Robots Hong Seong Park and Gurvinder Singh Virk 5 Human-robot shared workspace in aerospace factories Gilber Tang 6 Workspace sharing in mobile manipulation José Saenz 7 On rehabilitation robotics safety, benchmarking, standards Jan F. Veneman 8 A practical appraisal of ISO 13482 as a reference for an orphan robot category Paolo Barattini 9 Safety of Medical Robots, Regulation and Standards Kiyo Chinzei 10 The Other End of Human–Robot Interaction: Models for Safe and Efficient Tool–Tissue Interactions Arpad Takacs, Imre J. Rudas, Tamas Haidegger 11 Passive Bilateral Teleoperation with Safety Considerations Lorinc Marton 12 Human-Robot Interfaces in Autonomous Surgical Robots Paolo Fiorini and Riccardo Muradore

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisA hands-on textbook teaching how to carry out large-scale data analytics and implement machine learning solutions for big data. Including copious real-world examples, it offers a coherent teaching package with lab assignments, exercises, solutions for instructors, and lecture slides.Trade Review'With the growing ubiquity of large and complex datasets, MapReduce and Spark's dataflow programming models have become mission-critical skills for data scientists, data engineers, and ML engineers. Triguero and Galar leverage their extensive teaching experience on this topic to deliver this tour de force deep dive into both the technical concepts and programming knowhow needed for such modern large-scale data analytics. They interleave intuitive exposition of the concepts and examples from data engineering and classical ML pipelines with well-thought-out hands-on code and outputs. This book not only shows how all this knowledge is useful in practice today but also sets up the reader to be able to successfully 'generalize' to future workloads.' Arun Kumar, University of California, San DiegoTable of ContentsPart I. Understanding and Dealing with Big Data: 1. Introduction; 2. MapReduce; Part II. Big Data Frameworks: 3. Hadoop; 4. Spark; 5. Spark SQL and DataFrames; Part III. Machine Learning for Big Data: 6. Machine Learning with Spark; 7. Machine Learning for Big Data; 8. Implementing Classical Methods: k-means and Linear Regression; 9. Advanced Examples: Semi-supervised, Ensembles, Deep Learning Model Deployment.

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Book SynopsisImage processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. It provides a solid foundation for readers interested in understanding the core principles and practical applications of image prTable of ContentsPreface Volume 1. 1. Pixel Operations. 2. Spatial Filtering. 3. Edge Detection. 4. Segmentation and Processing of Binary Images. 5. Corner Detection. 6. Line Detection. Index.

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Book SynopsisThis hands-on guide presents three problem-focused ways to learn ML: no code using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. You'll learn key ML concepts by using real-world datasets with realistic problems.

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Book SynopsisAn engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep learningFulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.

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Book SynopsisIn recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves both as a starting point for researchers and graduate students in computer science, mathematics, and statistics trying to get into the field and as an invaluable reference for future research.Table of Contents1. The modern mathematics of deep learning Julius Berner, Philipp Grohs, Gitta Kutyniok and Philipp Petersen; 2. Generalization in deep learning Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio; 3. Expressivity of deep neural networks Ingo Gühring, Mones Raslan and Gitta Kutyniok; 4. Optimization landscape of neural networks René Vidal, Zhihui Zhu and Benjamin D. Haeffele; 5. Explaining the decisions of convolutional and recurrent neural networks Wojciech Samek, Leila Arras, Ahmed Osman, Grégoire Montavon and Klaus-Robert Müller; 6. Stochastic feedforward neural networks: universal approximation Thomas Merkh and Guido Montúfar; 7. Deep learning as sparsity enforcing algorithms A. Aberdam and J. Sulam; 8. The scattering transform Joan Bruna; 9. Deep generative models and inverse problems Alexandros G. Dimakis; 10. A dynamical systems and optimal control approach to deep learning Weinan E, Jiequn Han and Qianxiao Li; 11. Bridging many-body quantum physics and deep learning via tensor networks Yoav Levine, Or Sharir, Nadav Cohen and Amnon Shashua.

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Book SynopsisToday's social and behavioral researchers increasingly need to know: What do I do with all this data? This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter Computational Time and Resources sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.Trade Review"Current, highly informative, and useful, this is a 'go-to' book for social science graduate students, faculty, and practitioners seeking a strong introduction to machine learning. Unlike typical, more technical machine learning books, this one is unique in providing the strong psychological measurement guidance required to apply these techniques most appropriately. It walks the reader through general principles of machine learning, regression- and tree-based predictive models, text- and network-based methods of clustering, and--most innovatively--machine learning–based psychometric approaches (CFA and SEM)."--Fred Oswald, PhD, Professor and Herbert S. Autrey Chair in Social Sciences, Department of Psychological Sciences, Rice University "This book is very timely. Social scientists need to be educated about the pros and cons of machine learning methods and about how, when, and why these methods can be applied to their research topics. The book describes key techniques in enough detail to enable readers to subsequently digest more specialized journal articles or software applications, but not in so much detail as to lose momentum."--Sonya K. Sterba, PhD, Department of Psychology and Human Development, Vanderbilt University "Jacobucci, Grimm, and Zhang's ambitious book takes the reader on an in-depth tour of machine learning methods. Its strength is that the authors link machine learning to more traditional topics of regression, structural equation modeling, factor analysis, and network analysis methods. This book should be required reading for the new generation of psychology graduate students who are interested in more advanced quantitative methods."--James W. Pennebaker, PhD, Regents Centennial Professor of Liberal Arts and Professor of Psychology, The University of Texas at Austin ​"A 'must read' for social scientists who want to familiarize themselves with machine learning but don’t know where to start. Understanding the practices and principles of machine learning is fundamental to modern data analysis. Many social scientists will be surprised by how well their traditional statistical training has prepared them to grasp the material in the book."--Alexander Christensen, PhD, Department of Psychology and Human Development, Vanderbilt University-Table of ContentsI. Fundamental Concepts 1. Introduction - Why the Term Machine Learning? - Why do We Need Machine Learning? - How is this Book Different? - Definitions - Software - Datasets 2. The Principles of Machine Learning Research - Overview - Principle #1: Machine Learning is Not Just Lazy Induction - Principle #2: Orienting Our Goals Relative to Prediction, Explanation, and Description - Principle #3: Labeling a Study as Exploratory or Confirmatory is too Simplistic - Principle #4: Report Everything - Summary 3. The Practices of Machine Learning - Comparing Algorithms and Models - Model Fit - Bias-Variance Tradeoff - Resampling - Classification - Conclusion II. Algorithms for Univariate Outcomes 4. Regularized Regression - Linear Regression - Logistic Regression - Regularization - Rationale for Regularization - Alternative Forms of Regularization - Bayesian Regression - Summary 5. Decision Trees - Introduction - Decision Tree Algorithms - Miscellaneous Topics 6. Ensembles - Bagging - Random Forests - Gradient Boosting - Interpretation - Empirical Example - Important Notes - Summary III. Algorithms for Multivariate Outcomes 7. Machine Learning and Measurement - Defining Measurement Error - Impact of Measurement Error - Assessing Measurement Error - Weighting - Alternative Methods - Summary 8. Machine Learning and Structural Equation Modeling - Latent Variables as Predictors - Predicting Latent Variables - Using Latent Variables as Outcomes and Predictors - Can Regularization Improve Generalizability in SEM? - Nonlinear Relationships and Latent Variables - Summary 9. Machine Learning with Mixed-Effects Models - Mixed-Effects Models - Machine Learning with Clustered Data - Regularization with Mixed-Effects Models - Illustrative Example - Additional Strategies for Mining Longitudinal Data - Summary 10. Searching for Groups - Finite Mixture Model - Structural Equation Model Trees - Summary IV. Alternative Data Types 11. Introduction to Text Mining - Key Terminology - Data - Basic Text Mining - Text Data Preprocessing - Basic Analysis of the Teaching Comment Data - Sentiment Analysis - Topic Models - Summary 12. Introduction to Social Network Analysis - Network Visualization - Network Statistics - Basic Network Analysis - Network Modeling - Summary References

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Book SynopsisGet started with artificial intelligence for medical sciences and psychology. This book will help healthcare professionals and technologists solve problems using machine learning methods, computer vision, and natural language processing (NLP) techniques. The book covers ways to use neural networks to classify patients with diseases. You will know how to apply computer vision techniques and convolutional neural networks (CNNs) to segment diseases such as cancer (e.g., skin, breast, and brain cancer) and pneumonia. The hidden Markov decision making process is presented to help you identify hidden states of time-dependent data. In addition, it shows how NLP techniques are used in medical records classification. This book is suitable for experienced practitioners in varying medical specialties (neurology, virology, radiology, oncology, and more) who want to learn Python programming to help them work efficiently. It is also intended for data scientists, machine leTable of ContentsChapter 1: An Introduction to Artificial Intelligence for Medical SciencesChapter goal: This is the initial chapter. Subsequently, it encapsulates the specific context and structure of the book. Then, it states the varying medical specialties central to this book. Likewise, it properly presents independent subsets of artificial intelligence. Besides that, it unveils valuable tools for undertaking exercises; Python programming language, distribution package, and libraries. Afterward, it sufficiently acquaints you with different algorithms, including when to carry them out.Sub-topics:● Context of the book.● The book’s central point.● Artificial Intelligence subsets covered in this book.● Structure of the book.● Tools that this book implements.○ Python distribution package.○ Anaconda distribution package.○ Jupyter Notebook.○ Python libraries.● Encapsulating Artificial Intelligence.● Debunking algorithms.● Debunking supervised algorithms.● Debunking unsupervised algorithms.● Debunking Artificial Neural Networks.Chapter 2: Realizing Patterns in Common Diseases with Neural NetworksChapter goal: This chapter purportedly contains the application of artificial neural networks in modelling medical data. It properly instigates deep belief networks to model data and predicts whether a patient suffers from an ordinary disease (i.e., pneumonia and diabetes). Equally, it appraises the networks with fundamental metrics to discern the magnitude to which the networks set apart patients who suffer from the disease from those who do not.Sub-topics:● Classifying patients’ Cardiovascular disease diagnosis outcome data by executing a deepbelief network.● Preprocessing the Cardiovascular disease diagnosis outcome data.● Debunking deep belief networks.o Designing the deep belief network.o Relu Activation function.o Sigmoid activation function.● Training the deep belief network.● Outlining the deep belief networks predictions.● Considering the deep belief network’s performance.● Classifying patients’ diabetes diagnosis outcome data by executing a deep belief network.● Outlining the deep belief networks predictions .● Considering the deep belief network’s performance.● Conclusion.Chapter 3: A Case for COVID-19 Identifying Hidden States and Simulation ResultsChapter goal: This chapter instigates a set of series analysis methods to uniquely discern patterns in the US COVID-19 confirmed cases. To begin with, the Gaussian Hidden Markov Model inherits the series data, models it and identifies the hidden states, including the means and covariance in those states. Subsequently, the Monte Carlo simulation method replicates US COVID-19 confirmed cases across multiple trials, thus providing us with a rich comprehending of the patternChapter content:● Debunking the Hidden Markov Model● Descriptive analysis● Carrying Out the Gaussian Hidden Markov Modelo Considering the Hidden States in US COVID-19 Confirmed Cases with the GaussianHidden Markov Model● Simulating US COVID-19 Confirmed Cases with the Monte Carlo Simulation Methodo US COVID-19 confirmed cases simulation results● ConclusionChapter 4: Cancer Segmentation with Neural NetworksChapter goal: This chapter typically exhibits the practical application of computer vision andconvolutional neural networks for breast and skin Cancer realization and segmentation. Equally, it shows an approach to filter medical scans by applying canny, luplican, and sobel filters. It concludes by ascertaining the extent to which the networks accurately differentiate scans of patients with and without Cancer.Chapter content:● Debunking Cancer.● Debunking Skin Cancer● Depicting scans of a patient with Skin Cancer.● Classifying Patients’ Skin Cancer Diagnosis Image Data by Executing a Convolutional Neural Network.o Preprocessing the training Skin Cancer Image Data.o Preprocessing the Validation Skin Cancer Image Data.o Generating the Training Skin Cancer Diagnosis Image Data.o Tuning the Training Skin Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Skin CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.o Debunking Breast Cancer.● Classifying Ultrasound Scans of Breast Cancer Patients by Executing a Convolutional Neural Network.o Preprocessing the Validation Breast Cancer Image Data .o Preprocessing the Validation Breast Cancer Image Data .o Generating the Training Breast Cancer Diagnosis Image Data.o Tuning the Training Breast Cancer Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Breast CancerDiagnosis Image Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 5: Modelling Magnetic Resonance Imaging and X-Rays by Carrying out Artificial Neural NetworksChapter goal: This chapter intimately acquaints you with the practical application of computer vision and artificial neural networks in neurology and radiology. It promptly carries out convolutional neural networks for image classification. The initial network models MRI scans to set apart patients with and without a brain tumor, and the second network models X-ray scans to set apart patients with and without pneumonia. Besides that, it unveils an effective technique for appraising networks in medical image classification.Sub-topics:● Debunking Brain Tumors.● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Executing aConvolutional Neural Network.o Depicting MRI Scan of Patients with a Brain Tumor.o Depicting Brain Scans without a Brain Tumor.o Preprocessing the Training MRI Image Data.o Preprocessing the Validation MRI Image Data.o Generating the Training MRI Image Data.o Tuning the Training MRI Image Data.o Executing the Convolutional Neural Network to Classify Patients’ MRI Image Data.o Considering the Convolutional Neural Network’s Performance.● Debunking Pneumonia.o Classifying Patients’ CT scan Data by Executing a Convolutional Neural Network.o Depicting an X-Ray scan of a Patient with Pneumonia.o Depicting an X-Ray scan of a Patient without Pneumonia.o Processing the X-Ray Image Data.o Generating the Training Chest X-Ray Image Data.o Preprocessing the Validation Chest X-Ray Image Data.o Generating the Validation Chest X-Ray Image Data.o Tuning the Training Chest X-Ray Image Data.o Executing the Convolutional Neural Network to Classify Patients’ Chest X-Ray ImageData.▪ Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 6: A Case for COVID-19 CT Scan SegmentationChapter goal: This chapter presents an approach for carrying out convolutional neural networks to model chest CT scan images and differentiate between patients with and without COVID-19.Sub-topics:● Classifying Patients’ Model Magnetic Resonance Imaging (MRI) Data by Carrying out aConvolutional Neural Network.o Depicting a Chest CT scan of a COVID-19 Negative Patient.o Depicting a CT scan of COVID-19 Negative Patient.o Preprocessing the Training COVID-19 Data.o Preprocessing the Validation COVID-19 CT Scan Data.o Generating the Training COVID-19 CT Scan Data.o Tuning the Training COVID-19 CT Scan Data.● Data.o Considering the Convolutional Neural Network’s Performance.● Conclusion.Chapter 7 Modelling Clinical Trial DataChapter goal: This chapter familiarizes you with the prime essentials of the most widespread method for adequately investigating data from a clinical trial, recognized as a survival method. It debunks the Nelson-Aalen additive model. To begin with, it encapsulates the method. Subsequently, it promptly presents exploratory analysis, then correlation analysis by carrying out the Pearson correlation method. Following that, it outlines the survival table, then fits the model. It concludes by carefully outlining the profile table, confidence interval, and reproducing the cumulative and baseline hazard.sub-topics:● Debunking Clinical Trials.● An Overview of Survival Analysis.● Context of the Chapter.● Exploring the Nelson-Aalen Additive Model.● Descriptive Analysis.● Realizing a Correlation Relationship.● Outlining the Survival Table.● Carrying out the Nelson-Aalen Additive Model.o Outlining the Nelson-Aalen additive Model’s Confidence Intervalo Discerning the Survival Hazard.o Discerning the Cumulative Survival Hazard.o Baseline Survival Hazard.● Conclusion.● References.Chapter 8: Medical Record CategorizationChapter goal: This chapter sufficiently apprises a wholesome approach for realizing patterns in medical records by carrying out a linear discriminant analysis model. To begin with, it summarizes medical recording. Subsequently, it exhibits a technique of cleansing textual data by carrying out fundamental methods like regularization and TfidfVectorizer. Afterward, it executes the method to classify the medical specialty, then it assesses the extent to which it segregates classes.Sub-topics:● Medical Records.● Context of Chapter.● Debunking Categorization with Linear Discriminant Analysis.o Descriptive Statistics.o Preprocessing the Medical Records Data.o Carrying out Regular Expression.o Carrying Out Word Vectorization.o Carrying out the Linear Discriminant Analysis Model to Classify Patients’ MedicalRecords.o Considering the Linear Discriminant Analysis Model’s Performance.● Conclusion.Chapter 9: A Case for Psychology: Factoring and Clustering Personality DimensionsChapter goal: This chapter introduces you to analyzing the underlying patterns in human behavior by promptly carrying out exploratory factor analysis and cluster analysis. To begin with, it covers the big five personality dimensions. Following that, it presents an approach for typically collecting data by retaining a Likert scale and measuring the reliability of the scale with Cronbach’s reliability testing strategy. Subsequently, it performs factor analysis; beginning with estimating Bartlett Sphericity statistics, then the Kaiser-Meyer-Olkin statistic. Following that, it rotates the eigenvalues by carrying out the varimax rotation method and estimates the proportional variances and cumulative variances. In addition, it executes the K-Means method to observe clusters in the data; beginning with standardizing the data and carrying out principal component analysis.Sub-topics:● Debunking Personality Dimensions.● Questionnaires.● Likert Scale.● Reliability.o Spearman-Brown Reliability Testing Strategy.o Carrying out Cronbach's Reliability Testing Strategy.● Carrying out Factor Model.o Carrying out the Bartlett Sphericity Test.o Carrying out the Kaiser-Meyer-Olkin Test.o Discerning K with a Scree Plot.o Carrying out Eigenvalue Rotation.▪ Varimax Rotation.● Carrying out Cluster Analysis.o Carrying out Principal Component Analysis.O Returning K-Means label.

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Book SynopsisHow can machine learningespecially deep neural networksmake a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

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Book SynopsisThis updated second edition describes the intuition behind deep learning innovations without jargon or complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own.

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Book SynopsisThis easy-to-use reference for Tensorflow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.

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Book SynopsisBy moving away from flawed statistical methodologies, you'll move toward an intuitive view of probability as a mathematically rigorous statistical framework that quantifies uncertainty holistically and successfully. This book shows you how.

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Book SynopsisToday, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system.The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solvTable of ContentsIntroduction. How Can Machine Learning Solve my Problem? 1. A Murder Mystery 2. Assessing People’s Skills Interlude. The Machine Learning Life Cycle 3. Meeting Your Match 4. Uncluttering Your Inbox 5. Making Recommendations 6. Understanding Asthma 7. Harnessing the Crowd 8. How to Read a Model Afterword

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Book SynopsisThe healthcare industry is predominantly moving towards affordable, accessible, and quality health care. All organizations are striving to build communication compatibility among the wide range of devices that have operated independently. Recent developments in electronic devices have boosted the research in the medical imaging field. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. Despite the significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer. Machine Learning and AI Techniques in Interactive Medical Image Analysis discusses how clinical efficiency can be improved by investigating the different types of intelligent techniques and systems to get more reliable and accurate diagnostic conclusions. This book further introduces segmentation techniques to locate suspicious areas in medical images and increase the segmentation accuracy. Covering topics such as computer-aided detection, intelligent techniques, and machine learning, this premier reference source is a dynamic resource for IT specialists, computer scientists, diagnosticians, imaging specialists, medical professionals, hospital administrators, medical students, medical technicians, librarians, researchers, and academicians.

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Book SynopsisExpand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book Load, store, edit, and visualize data using OpenCV and Python Grasp the fundamental concepts of classification, regression, and clustering Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Evaluate, compare, and choose the right algorithm for any taskWho This Book Is ForThis book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineeringIn DetailMachine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!Style and approachOpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.

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Book SynopsisDiscover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey Features Implement deep learning algorithms to build AI models with the help of tips and tricks Understand how deep learning models operate using expert techniques Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets Book DescriptionDeep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them.This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.What you will learn Learn how to create binary and multi-class deep neural network models Implement GANs for generating new images Create autoencoder neural networks for image dimension reduction, image de-noising and image correction Implement deep neural networks for performing efficient text classification Learn to define a recurrent convolutional network model for classification in Keras Explore best practices and tips for performance optimization of various deep learning models Who this book is forThis book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.Table of ContentsTable of Contents Revisiting Deep Learning architecture and techniques Deep Neural Networks for multiclass classification Deep Neural Networks for regression Image classification and recognition Image classification using convolutional neural networks Applying Autoencoder neural networks using Keras Image classification for small data using transfer learning Creating new images using generative adversarial networks Deep network for text classification Text classification using recurrent neural networks Text classification using Long Short-Term Memory Network Text classification using convolutional recurrent networks Tips, tricks and the road ahead

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Book SynopsisGet started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activitiesKey Features Understand the fundamentals of tensors, neural networks, and deep learning Discover how to implement and fine-tune deep learning models for real-world datasets Build your experience and confidence with hands-on exercises and activities Book DescriptionGetting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging.If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running. You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing.By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.What you will learn Get to grips with TensorFlow’s mathematical operations Pre-process a wide variety of tabular, sequential, and image data Understand the purpose and usage of different deep learning layers Perform hyperparameter-tuning to prevent overfitting of training data Use pre-trained models to speed up the development of learning models Generate new data based on existing patterns using generative models Who this book is forThis TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.Table of ContentsTable of Contents Introduction to Machine Learning with TensorFlow Loading and Processing Data TensorFlow Development Regression and Classification Models Classification Models Regularization and Hyperparameter Tuning Convolutional Neural Networks Pre-Trained Networks Recurrent Neural Networks Custom TensorFlow Components Generative Models

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Book SynopsisThis book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines. Table of Contents1. Unsupervised Visual Learning: from Pixels to Seeing.- 2. Unsupervised Learning of Graph and Hypergraph Matching.- 3. Unsupervised Learning of Graph and Hypergraph Clustering.- 4. Feature Selection meets Unsupervised Learning.- 5. Unsupervised Learning of Object Segmentation in Video with Highly Probable Positive Features.- 6. Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation through Space and Time.- 7. Unsupervised Learning in Space and Time over Several Generations of Teacher and Student Networks.- 8. Unsupervised Learning Towards the Future.

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a huge range and FREE tracked UK delivery on ALL orders.

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Book SynopsisThis book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future.An Intuitive Exploration of Artificial Intelligence offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense potential.The book is intended to serve as a textbook for graduate and senior-level undergraduate courses in AI. Moreover, since the book provides a strong geometrical intuition about advanced mathematical foundations of AI, practitioners and researchers will equally benefit from the book.Table of ContentsPart I, Foundations.- AI Sculpture.- Make Me Learn.- Images and Sequences.- Why AI Works.- Learning to Sculpt.- Unleashing the Power of Generation.- The Road Most Rewarded.- The Classical World.- Part II, Applications.- To See is to Believe.- Read, Read, Read.- Lend Me Your Ear.- Create Your Shire and Rivendell.- Math to Code to Petaflops.- AI and Business.- Part III, Road Ahead.- Keep Marching on.- Benevolent AI for All.- Am I Looking at Myself?.- App. A, Solutions.- Further Reading.- Acronyms.- Glossary.- References.- Index.

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Book SynopsisThis book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others.Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI.Beginning with examples of what Explainable AI (XAI) is and why it is needed in the field, the book details different approaches to XAI depending on specific context and need. Hands-on work on interpretable models with specific examples leveraging Python are then presented, showing how intrinsic interpretable models can be interpreted and how to produce “human understandable” explanations. Model-agnostic methods for XAI are shown to produce explanations without relying on ML models internals that are “opaque.” Using examples from Computer Vision, the authors then look at explainable models for Deep Learning and prospective methods for the future. Taking a practical perspective, the authors demonstrate how to effectively use ML and XAI in science. The final chapter explains Adversarial Machine Learning and how to do XAI with adversarial examples.Table of ContentsContents1. The Landscape1.1 Examples of what Explainable AI is1.1.1 Learning Phase1.1.2 Knowledge Discovery1.1.3 Reliability and Robustness1.1.4 What have we learnt from the 3 examples1.2 Machine Learning and XAI1.2.1 Machine Learning tassonomy1.2.2 Common Myths1.3 The need for Explainable AI1.4 Explainability and Interpretability: different words to say the same thing or not?1.4.1 From World to Humans1.4.2 Correlation is not causation1.4.3 So what is the difference between interpretability and explainability?1.5 Making Machine Learning systems explainable1.5.1 The XAI flow1.5.2 The big picture1.6 Do we really need to make Machine Learning Models explainable?1.7 Summary1.8 References2. Explainable AI: needs, opportunities and challenges2.1 Human in the loop2.1.1 Centaur XAI systems2.1.2 XAI evaluation from “Human in The Loop perspective”2.2 How to make Machine Learning models explainable2.2.1 Intrinsic Explanations2.2.2 Post-Hoc Explanations2.2.3 Global or Local Explainability2.3 Properties of Explanations2.4 Summary2.5 References3 Intrinsic Explainable Models3.1.Loss Function3.2.Linear Regression3.3.Logistic Regression3.4.Decision Trees3.5.K-Nearest Neighbors (KNN)3.6.Summary3.7 References4. Model-agnostic methods for XAI4.1 Global Explanations: permutation Importance and Partial Dependence Plot4.1.1 Ranking features by Permutation Importance4.1.2 Permutation Importance on the train set4.1.3 Partial Dependence Plot4.1.4 Properties of Explanations4.2 Local Explanations: XAI with Shapley Additive explanations4.2.1 Shapley Values: a game-theoretical approach4.2.2 The first use of SHAP4.2.3 Properties of Explanations4.3 The road to KernelSHAP4.3.1 The Shapley formula4.3.2 How to calculate Shapley values4.3.3 Local Linear Surrogate Models (LIME)4.3.4 KernelSHAP is a unique form of LIME4.4 Kernel SHAP and interactions4.4.1 The NewYork Cab scenario4.4.2 Train the Model with preliminary analysis4.4.3 Making the model explainable with KernelShap4.4.4 Interactions of features4.5 A faster SHAP for boosted trees4.5.1 Using TreeShap4.5.2 Providing explanations4.6 A naïve criticism to SHAP4.7 Summary4.8 References5. Explaining Deep Learning Models5.1 Agnostic Approach5.1.1 Adversarial Features5.1.2 Augmentations5.1.3 Occlusions as augmentations5.1.4 Occlusions as an Agnostic XAI Method5.2 Neural Networks5.2.1 The neural network structure5.2.2 Why the neural network is Deep? (vs shallow)5.2.3 Rectified activations (and Batch Normalization)5.2.4 Saliency Maps5.3 Opening Deep Networks5.3.1 Different layer explanation5.3.2 CAM (Class Activation Maps) and Grad-CAM5.3.3 DeepShap / DeepLift5.4 A critic of Saliency Methods5.4.1 What the network sees5.4.2 Explainability batch normalizing layer by layer5.5 Unsupervised Methods5.5.1 Unsupervised Dimensional Reduction5.5.2 Dimensional reduction of convolutional filters5.5.3 Activation Atlases: How to tell a wok from a pan5.6 Summary5.7 References6. Making science with Machine Learning and XAI6.1 Scientific method in the age of data6.2 Ladder of Causation6.3 Discovering physics concepts with ML and XAI6.3.1 The magic of autoencoders6.3.2 Discover the physics of damped pendulum with ML and XAI6.3.3 Climbing the ladder of causation6.4 Science in the age of ML and XAI6.5 Summary6.6 References7. Adversarial Machine Learning and Explainability7.1 Adversarial Examples (AE) crash course7.1.2 Hands-on Adversarial Examples7.2 Doing XAI with Adversarial Examples7.3 Defending against Adversarial Attacks with XAI7.4 Summary7.5 References8. A proposal for a sustainable model of Explainable AI8.1 The XAI "fil rouge"8.2 XAI and GDPR8.2.1 FAST XAI8.3 Conclusions8.4 Summary8.5 ReferencesIndex

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Book SynopsisThis book contains a selection of the best papers of the 32nd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2020, held in Leiden, The Netherlands, in November 2020. Due to the COVID-19 pandemic the conference was held online. The 12 papers presented in this volume were carefully reviewed and selected from 41 regular submissions. They address various aspects of artificial intelligence such as natural language processing, agent technology, game theory, problem solving, machine learning, human-agent interaction, AI and education, and data analysis.The chapter 11 is published open access under a CC BY license (Creative Commons Attribution 4.0 International License) Chapter “Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.. Table of ContentsEvaluating the Robustness of Question-Answering Models to Paraphrased Questions.- FlipOut: Uncovering Redundant Weights via Sign Flipping.- Evolving Virtual Embodied Agents using External Artifact Evaluations.- Continuous Surrogate-based Optimization Algorithms are Well-suitedfor Expensive Discrete Problems.- Comparing Correction Methods to Reduce Misclassification Bias.- A Spiking Neuron Implementation of Genetic Algorithms for Optimization.- Solving Hofstadter's Analogies using Structural Information Theory.- A Semantic Tableau Method for Argument Construction.- `Thy algorithm shalt not bear false witness': An Evaluation of Multiclass Debiasing Methods on Word Embeddings.- An Intelligent Tree Planning Approach using Location-based Social Networks Data.- Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning.- Swarm Construction Coordinated through the Building Material.

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Book SynopsisThis book constitutes the refereed proceedings of the 27th China Conference on Information Retrieval, CCIR 2021, held in Dalian, China, in October 2021.The 15 full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections: search and recommendation, NLP for IR, IR in Education, and IR in Biomedicine.Table of ContentsSearch and Recommendation.- NLP for IR.- IR in Education.- IR in Biomedicine.

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Book SynopsisThe two volume set LNCS 13052 and 13053 constitutes the refereed proceedings of the 19th International Conference on Computer Analysis of Images and Patterns, CAIP 2021, held virtually, in September 2021. The 87 papers presented were carefully reviewed and selected from 129 submissions. The papers are organized in the following topical sections across the 2 volumes: 3D vision, biomedical image and pattern analysis; machine learning; feature extractions; object recognition; face and gesture, guess the age contest, biometrics, cryptography and security; and segmentation and image restoration.Table of Contents3D Vision.- Simultaneous Bi-Directional Structured Light Encoding for Practical Uncalibrated Profilometry.- Joint Global ICP for Improved Automatic Alignment of Full Turn Object Scans.- Fast Projector-Driven Structured Light Matching in Sub-Pixel Accuracy using Bilinear Interpolation Assumption.- Pyramidal Layered Scene Inference with Image Outpainting for Monocular View Synthesis.- Out of the Box: Embodied Navigation in the Real World.-Toward a novel LSB-based collusion-secure fingerprinting schema for 3D video.- A Combinatorial Coordinate System for the Vertices in the Octagonal C4C8 ( R) Grid.- Bilingual Speech Recognition by Estimating Speaker Geometry from Video Data.- Cost-efficient Color Correction Approach on Uncontrolled Lighting Conditions.- HPA-Net: Hierarchical and Parallel Aggregation Network for Context Learning in Stereo Matching.- MTStereo 2.0: accurate stereo depth estimation via Max-tree matching.- Biomedical Image and Pattern Analysis.- H-OCS: a hybrid optic cup segmentation of retinal images.- Retinal Vessel Segmentation using Blending-based Conditional Generative Adversarial Networks.- U-shaped densely connected Convolutions for Left ventricle segmentation from CMR images.- Deep Learning approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images.- Shape Analysis Approach towards Assessment of Cleft Lip Repair Outcome.- MMEC: Multi-Modal Ensemble Classifier for Protein Secondary Structure Prediction.- Breast Cancer Brain Metastasis: Automated MRI Image Analysis for the Prediction of Primary Cancer Using Radiomics.- An Adaptive Semi-Automated Integrated System for Multiple Sclerosis Lesion Segmentation in Longitudinal MRI Scans Based on a Convolutional Neural Network.- A Three-Dimensional Reconstruction Integrated System for Brain Multiple Sclerosis Lesions.- Rule Extraction in the Assessment of Brain MRI Lesions in Multiple Sclerosis: Preliminary Findings.- Invariant Moments, Textural and Deep features for Diagnostic MR and CT Image Retrieval.- Toward multiwavelet Haar-Schauder entropy for biomedical signal reconstruction.- Machine Learning.- Handling Missing Observations with an RNN-based Prediction-Update Cycle.- eGAN: Unsupervised approach to class imbalance using transfer learning.- Progressive Contextual Excitation for Smart Farming Application.- Fine-Grained Image Classification for Pollen Grain Microscope Images.- Adaptive Style Transfer Using SISR.- Object-Centric Anomaly Detection using Memory Augmentation.- Document Language Classification: Hierarchical Model With Deep Learning Approach.- Parsing Digitized Vietnamese Paper Documents.- EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task.- When Deep Learners Change Their Mind: Learning Dynamics for Active Learning.- Learning to Navigate in the Gaussian Mixture Surface.- A Deep Hybrid Approach For Hate Speech Analysis.- On improving generalization of CNN-based image classification with delineation maps using the CORF push-pull inhibition operator.- Fast Hand Detection in Collaborative Learning Environments.- Assessing the Role of Boundary-level Objectives in Indoor Semantic Segmentation.- Skin lesion classification using convolutional neural networks based on Multi-Features Extraction.- Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing.- Layer-wise Relevance Propagation based Sample Condensation for Kernel Machines.-

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Book SynopsisSemi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and OutlookTable of ContentsIntroduction to Statistical Machine Learning.- Overview of Semi-Supervised Learning.- Mixture Models and EM.- Co-Training.- Graph-Based Semi-Supervised Learning.- Semi-Supervised Support Vector Machines.- Human Semi-Supervised Learning.- Theory and Outlook.

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Book SynopsisAnswer Set Programming (ASP) is a declarative problem solving approach, initially tailored to modeling problems in the area of Knowledge Representation and Reasoning (KRR). More recently, its attractive combination of a rich yet simple modeling language with high-performance solving capacities has sparked interest in many other areas even beyond KRR. This book presents a practical introduction to ASP, aiming at using ASP languages and systems for solving application problems. Starting from the essential formal foundations, it introduces ASP's solving technology, modeling language and methodology, while illustrating the overall solving process by practical examples. Table of Contents: List of Figures / List of Tables / Motivation / Introduction / Basic modeling / Grounding / Characterizations / Solving / Systems / Advanced modeling / ConclusionsTable of ContentsList of Figures.- List of Tables.- Motivation.- Introduction.- Basic modeling.- Grounding.- Characterizations.- Solving.- Systems.- Advanced modeling.- Conclusions.

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