This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. What You Will Learn Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.

This book explores the application of data mining and machine learning techniques in studying the activity pattern, decision-making skills, misconducts, and actions resulting in the intervention of VAR in European soccer leagues referees. The game of soccer at the elite level is characterised by intense competitions, a high level of intensity, technical, and tactical skills coupled with a long duration of play. Referees are required to officiate the game and deliver correct and indisputable decisions throughout the duration of play. The increase in the spatial and temporal task demands of the game necessitates that the referees must respond and cope with the physiological and psychological loads inherent in the game. The referees are also required to deliver an accurate decision and uphold the rules and regulations of the game during a match. These demands and attributes make the work of referees highly complex. The increasing pace and complexity of the game resulted in the introduction of the Video Assistant Referee (VAR) to assist and improve the decision-making of on-field referees. Despite the integration of VAR into the current refereeing system, the performances of the referees are yet to be error-free. Machine learning coupled with data mining techniques has shown to be vital in providing insights from a large dataset which could be used to draw important inferences that can aid decision-making for diagnostics purposes and overall performance improvement. A total of 6232 matches from 5 consecutive seasons officiated across the English Premier League, Spanish LaLiga, Italian Serie A as well as the German Bundesliga was studied. It is envisioned that the findings in this book could be useful in recognising the activity pattern of top-class referees, that is non-trivial for the stakeholders in devising strategies to further enhance the performances of referees as well as empower talent identification experts with pertinent information for mapping out future high-performance referees.

This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.

This book constitutes the refereed proceedings of the 10th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2022, held in Dubai, UAE, in November 2022. The 16 revised full papers presented were carefully reviewed and selected from 24 submissions. The conference presents papers on subject such as pattern recognition and machine learning based on artificial neural networks.

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slides and solutions for instructors, this is the ideal introduction to machine learning for engineering students of all disciplines.

Computational neuroscience is the theoretical study of the brain to uncover the principles and mechanisms that guide the development, organization, information processing, and mental functions of the nervous system. Although not a new area, it is only recently that enough knowledge has been gathered to establish computational neuroscience as a scientific discipline in its own right. Given the complexity of the field, and its increasing importance in progressing our understanding of how the brain works, there has long been a need for an introductory text on what is often assumed to be an impenetrable topic. The new edition of Fundamentals of Computational Neuroscience build on the success and strengths of the previous editions. It introduces the theoretical foundations of neuroscience with a focus on the nature of information processing in the brain. The book covers the introduction and motivation of simplified models of neurons that are suitable for exploring information processing in large brain-like networks. Additionally, it introduces several fundamental network architectures and discusses their relevance for information processing in the brain, giving some examples of models of higher-order cognitive functions to demonstrate the advanced insight that can be gained with such studies. Each chapter starts by introducing its topic with experimental facts and conceptual questions related to the study of brain function. An additional feature is the inclusion of simple Matlab programs that can be used to explore many of the mechanisms explained in the book. An accompanying webpage includes programs for download. The book will be the essential text for anyone in the brain sciences who wants to get to grips with this topic.

This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.

[FIRST EDITION] This accessible textbook presents an introduction to computer vision algorithms for industrially-relevant applications of X-ray testing. Features: introduces the mathematical background for monocular and multiple view geometry; describes the main techniques for image processing used in X-ray testing; presents a range of different representations for X-ray images, explaining how these enable new features to be extracted from the original image; examines a range of known X-ray image classifiers and classification strategies; discusses some basic concepts for the simulation of X-ray images and presents simple geometric and imaging models that can be used in the simulation; reviews a variety of applications for X-ray testing, from industrial inspection and baggage screening to the quality control of natural products; provides supporting material at an associated website, including a database of X-ray images and a Matlab toolbox for use with the book's many examples.

This book meets the present and future needs for the interaction between various science and technology/engineering areas on the one hand and different branches of soft computing on the other. Soft computing is the recent development about the computing methods which include fuzzy set theory/logic, evolutionary computation (EC), probabilistic reasoning, artificial neural networks, machine learning, expert systems, etc. Soft computing refers to a partnership of computational techniques in computer science, artificial intelligence, machine learning, and some other engineering disciplines, which attempt to study, model, and analyze complex problems from different interdisciplinary problems. This, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximations. Interdisciplinary sciences include various challenging problems of science and engineering. Recent developments in soft computing are the bridge to handle different interdisciplinary science and engineering problems. In recent years, the correspondingly increased dialog between these disciplines has led to this new book. This is done, firstly, by encouraging the ways that soft computing may be applied in traditional areas, as well as point towards new and innovative areas of applications and secondly, by encouraging other scientific disciplines to engage in a dialog with the above computation algorithms outlining their problems to both access new methods as well as to suggest innovative developments within itself.

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN - two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, and loss functions. After completing this book, you will understand reinforcement learning with deep q and generative adversarial networks using the TensorFlow library. What You Will Learn Understand the fundamentals of reinforcement learning Apply reinforcement learning programming techniques to solve quantitative-finance problems Gain insight into convolutional neural networks and recurrent neural networks Understand the Markov decision process Who This Book Is ForData Scientists, Machine Learning engineers and Python programmers who want to apply reinforcement learning to solve problems.

Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn Understand the fundamental concepts of working with geodata Work with multiple geographical data types and file formats in Python Create maps in Python Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.

Get a hands-on introduction to Transformer architecture using the Hugging Face library. This book explains how Transformers are changing the AI domain, particularly in the area of natural language processing. This book covers Transformer architecture and its relevance in natural language processing (NLP). It starts with an introduction to NLP and a progression of language models from n-grams to a Transformer-based architecture. Next, it offers some basic Transformers examples using the Google colab engine. Then, it introduces the Hugging Face ecosystem and the different libraries and models provided by it. Moving forward, it explains language models such as Google BERT with some examples before providing a deep dive into Hugging Face API using different language models to address tasks such as sentence classification, sentiment analysis, summarization, and text generation. After completing Introduction to Transformers for NLP, you will understand Transformer concepts and be able to solve problems using the Hugging Face library. What You Will Learn Understand language models and their importance in NLP and NLU (Natural Language Understanding) Master Transformer architecture through practical examples Use the Hugging Face library in Transformer-based language models Create a simple code generator in Python based on Transformer architecture Who This Book Is ForData Scientists and software developers interested in developing their skills in NLP and NLU (Natural Language Understanding)

This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting; the search for new laws in economics, politics, sociology. Accumulating many years of experience in the development and application of numerical methods of symbolic regression to solving control problems, the authors offer new possibilities not only in the field of control automation, but also in the design of completely different optimal structures in many fields. For specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints scientists with the methods of automatic construction of control systems.For specialists in the field of machine learning, the book opens up a new, much broader direction than neural networks: methods of symbolic regression. This book makes it easy to master this new area in machine learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction of numerical methods for obtaining analytical solutions to unsolvable problems; for example, numerical analytical solutions of algebraic equations, differential equations, non-trivial integrals, etc. For specialists in the field of artificial intelligence, the book offers a machine way to solve problems, framed in the form of analytical relationships.

Emerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.

Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You'll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You'll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You'll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering. A section on Natural Language Processing (NLP) covers text processing, text mining, and embeddings for classification. This new edition also introduces Koalas in Spark and how to automate data workflow using Airflow and PySpark's latest ML library. After completing this book, you will understand how to use PySpark's machine learning library to build and train various machine learning models, along with related components such as data ingestion, processing and visualization to develop data-driven intelligent applications What you will learn: Build a spectrum of supervised and unsupervised machine learning algorithms Use PySpark's machine learning library to implement machine learning and recommender systems Leverage the new features in PySpark's machine learning library Understand data processing using Koalas in Spark Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit models Who This Book Is For Data science and machine learning professionals.

The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications.

The four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications.

This book constitutes revised selected papers from the refereed proceedings of the 11th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2021, held as a virtual event during December 16-18, 2021. The 13 full papers included in this book were carefully reviewed and selected from 17 submissions. They were organized in topical sections as follows: Computational advances in bio and medical sciences; and computational advances in molecular epidemiology.

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

Learn the science of collecting information to make effective decisions

Everyday decisions are made without the benefit of accurate information. "Optimal Learning "develops the needed principles for gathering information to make decisions, especially when collecting information is time-consuming and expensive. Designed for readers with an elementary background in probability and statistics, the book presents effective and practical policies illustrated in a wide range of applications, from energy, homeland security, and transportation to engineering, health, and business.

This book covers the fundamental dimensions of a learning problem and presents a simple method for testing and comparing policies for learning. Special attention is given to the knowledge gradient policy and its use with a wide range of belief models, including lookup table and parametric and for online and offline problems. Three sections develop ideas with increasing levels of sophistication: Fundamentals explores fundamental topics, including adaptive learning, ranking and selection, the knowledge gradient, and bandit problemsExtensions and Applications features coverage of linear belief models, subset selection models, scalar function optimization, optimal bidding, and stopping problems Advanced Topics explores complex methods including simulation optimization, active learning in mathematical programming, and optimal continuous measurements

Each chapter identifies a specific learning problem, presents the related, practical algorithms for implementation, and concludes with numerous exercises. A related website features additional applications and downloadable software, including MATLAB and the Optimal Learning Calculator, a spreadsheet-based package that provides an introduc-tion to learning and a variety of policies for learning.

Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment. What You Will Learn Apply adaptive algorithms to practical applications and examples Understand the relevant data representation features and computational models for time-varying multi-dimensional data Derive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real data Speed up your algorithms and put them to use on real-world stationary and non-stationary data Master the applications of adaptive algorithms on critical edge device computation applications Who This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.

This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry. By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effective Advanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.

This is the first book to provide a comprehensive introduction to a new semiparametric causal discovery approach known as LiNGAM, with the fundamental background needed to understand it. It offers a general overview of the basics of the LiNGAM approach for causal discovery, estimation principles, and algorithms. This semiparametric approach is one of the most exciting new topics in the field of causal discovery. The new framework assumes parametric assumptions on the functional forms of structural equations but makes no assumption on the distributions of exogenous variables other than non-Gaussianity. It provides data-analysis tools capable of estimating a much wider class of causal relations even in the presence of hidden common causes. This feature is in contrast to conventional nonparametric approaches based on conditional independence of variables. This book is highly recommended to readers who seek an in-depth and up-to-date overview of this new causal discovery approach to advance the technique as well as to those who are interested in applying this approach to real-world problems. This LiNGAM approach should become a standard item in the toolbox of statisticians, machine learners, and practitioners who need to perform observational studies.

This book provides its readers with an introduction to interesting prediction and science dynamics problems in the field of Science of Science. Prediction focuses on the forecasting of future performance (or impact) of an entity, either a research article or a scientist, and also the prediction of future links in collaboration networks or identifying missing links in citation networks. The single chapters are written in a way that help the reader gain a detailed technical understanding of the corresponding subjects, the strength and weaknesses of the state-of-the-art approaches for each described problem, and the currently open challenges. While chapter 1 provides a useful contribution in the theoretical foundations of the fields of scientometrics and science of science, chapters 2-4 turn the focal point to the study of factors that affect research impact and its dynamics. Chapters 5-7 then focus on article-level measures that quantify the current and future impact of scientific articles. Next, chapters 8-10 investigate subjects relevant to predicting the future impact of individual researchers. Finally, chapters 11-13 focus on science evolution and dynamics, leveraging heterogeneous and interconnected data, where the analysis of research topic trends and their evolution has always played a key role in impact prediction approaches and quantitative analyses in the field of bibliometrics. Each chapter can be read independently, since it includes a detailed description of the problem being investigated along with a thorough discussion and study of the respective state-of-the-art. Due to the cross-disciplinary character of the Science of Science field, the book may be useful to interested readers from a variety of disciplines like information science, information retrieval, network science, informetrics, scientometrics, and machine learning, to name a few. The profiles of the readers may also be diverse ranging from researchers and professors in the respective fields to students and developers being curious about the covered subjects.