Machine learning

This book starts with the rationale for creating an FCM by contrast to other techniques for participatory modeling, as this rationale is a key element to justify the adoption of techniques in a research paper. Fuzzy cognitive mapping is an active research field with over 20,000 publications devoted to externalizing the qualitative perspectives or ¿mental models¿ of individuals and groups. Since the emergence of fuzzy cognitive maps (FCMs) back in the 80s, new algorithms have been developed to reduce bias, facilitate the externalization process, or efficiently utilize quantitative data via machine learning. It covers the development of an FCM with participants through a traditional in-person setting, drawing from the experience of practitioners and highlighting solutions to commonly encountered challenges. The book continues with introducing principles of simulations with FCMs as a tool to perform what-if scenario analysis, while extending those principles to more elaborated simulation scenarios where FCMs and agent-based modeling are combined. Once an FCM model is obtained, the book then details the analytical tools available for practitioners (e.g., to identify the most important factors) and provides examples to aid in the interpretation of results. The discussion concerning relevant extensions is equally pertinent, which are devoted to increasing the expressiveness of the FCM formalism in problems involving uncertainty. The last four chapters focus on building FCM models from historical data. These models are typically needed when facing multi-output prediction or pattern classification problems. In that regard, the book smoothly guides the reader from simple approaches to more elaborated algorithms, symbolizing the noticeable progress of this field in the last 35 years. Problems, recent references, and functional codes are included in each chapter to provide practice and support further learning from practitioners and researchers.

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applicationsKey FeaturesWork with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithmsEvaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoringUncover effective approaches to deep learning for computer vision, time series analysis, and forecastingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionDiscover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications.By navigating the versatile machine learning tools in the MATLAB environment, you'll learn how to seamlessly interact with the workspace. You'll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you'll explore various classification and regression techniques, skillfully applying them with MATLAB functions.This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You'll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you'll leverage MATLAB tools for deep learning and managing convolutional neural networks.By the end of the book, you'll be able to put it all together by applying major machine learning algorithms in real-world scenarios.What you will learnDiscover different ways to transform data into valuable insightsExplore the different types of regression techniquesGrasp the basics of classification through Naive Bayes and decision treesUse clustering to group data based on similarity measuresPerform data fitting, pattern recognition, and cluster analysisImplement feature selection and extraction for dimensionality reductionHarness MATLAB tools for deep learning explorationWho this book is forThis book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.Table of ContentsExploring MATLAB for Machine LearningWorking with Data in MATLABPrediction Using Classification and RegressionClustering Analysis and Dimensionality ReductionIntroducing Artificial Neural Networks ModelingDeep Learning and Convolutional Neural NetworksNatural Language Processing Using MATLABMATLAB for Image Processing and Computer VisionTime Series Analysis and Forecasting with MATLABMATLAB Tools for Recommender SystemsAnomaly Detection in MATLAB

Probably almost correct (PAC) bounds have been an intensive field of research over the last two decades. Hundreds of papers have been published and much progress has been made resulting in PAC-Bayes bounds becoming an important technique in machine learning. The proliferation of research has made the field for a newcomer somewhat daunting. In this tutorial, the author guides the reader through the topic's complexity and large body of publications. Covering both empirical and oracle PAC-bounds, this book serves as a primer for students and researchers who want to get to grips quickly with the subject. It provides a friendly introduction that illuminates the basic theory and points to the most important publications to gain deeper understanding of any particular aspect.

This book constitutes the refereed proceedings of the 6th International Conference on Information and Knowledge Systems, ICIKS 2023, held in Portsmouth, UK, during June 22¿23, 2023.The 18 full papers and 6 short papers included in this book were carefully reviewed and selected from 58 submissions. They were organized in topical sections as follows: Decision Making, Recommender Systems, and Information Support Systems; Information Systems and Machine Learning; Knowledge Management, Context and Ontology; Cybersecurity and Intelligent Systems; and Natural Language Processing for Decision Systems.

This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known supervised machine learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.

Spin glass models were introduced by physicists in the 1970s to model the statistical properties of certain magnetic materials. Over the last half century, these models have motivated a blossoming line of mathematical work with applications to multiple fields, at first sight distant from physics. This tutorial is deliberately written in a somewhat non-standard style, from several viewpoints. Rather than developing the theory in the most general setting, the authors focus on two concrete problems that are motivated by questions in statistical estimation. Their treatment is far from exhaustive, but they do not hesitate to pursue detours that are interesting, but indirectly related to the original questions posed by the examples. The authors also present a mixture of non-rigorous and rigorous techniques. The authors clearly indicate when something is proven and explain non-rigorous techniques on examples for which rigorous alternatives are available. Written by two recognized experts and based on a course given at Stanford University, this tutorial is a unique introduction to a topic that has many avenues for furthering research in statistics, mathematics, and computer science. It provides an accessible tutorial to understand and use the theories being deployed in physics for over 50 years.

This book provides a comprehensive and systematic exploration of next-generation Edge Intelligence (EI) Networks. It delves deep into the critical design considerations within this context, emphasizing the necessity for functional and dependable interactions between networking strategies and the diverse application scenarios. This should help assist to encompass a wide range of environments.This book also discusses topics such as resource optimization, incentive mechanisms, channel prediction and cutting-edge technologies, which includes digital twins and advanced machine learning techniques. It underscores the importance of functional integration to facilitate meaningful collaborations between networks and systems, while operating across heterogeneous environments aiming support novel and disruptive human-oriented services and applications. Valuable insights into the stringent requirements for intelligence capabilities, communication latency and real-time response are discussed. This characterizes the new EI era, driving the creation of comprehensive cross-domain architectural ecosystems that infuse human-like intelligence into every aspect of emerging EI systems.This book primarily targets advanced-level students as well as postdoctoral researchers, who are new to this field and are searching for a comprehensive understanding of emerging EI systems. Practitioners seeking guidance in the development and implementation of EI systems in practical contexts will also benefit from this book.

This book gathers the proceedings of the 8th International Conference on Advancements of Medicine and Health Care through Technology, MEDITECH 2022, held virtually on 20¿22 October 2022, from Cluj-Napoca, Romania. It reports on both theoretical and practical developments in biomedical imaging and image processing, health technology, technologies for education, and biomedical signal processing and medical devices, measurements and instrumentation. Both the conference and the realization of this book were supported by the Romanian National Society for Medical Engineering and Biological Technology (SNIMTB).

This book describes, extends, and illustrates the metrics of binary classification through worked examples.Worked examples based on pragmatic test accuracy study data are used in chapters to illustrate relevance to day-to-day clinical practice. Readers will gain an understanding of sensitivity and specificity and predictive values along with many other parameters.The contents are highly structured, and the use of worked examples facilitates understanding and interpretation.This book is a resource for clinicians in any discipline who are involved in the performance or assessment of test accuracy studies and professionals in the disciplines of machine learning or informatics wishing to gain insight into clinical applications of 2x2 tables.

This book constitutes the refereed proceedings of the 1st Analytics Global Conference, AGC 2023, held in Kolkata, India, in April 2023. The AGC conference sought to facilitate industry-academia interfacing in the domain of machine learning and artificial intelligence.The 11 full papers presented in these proceedings were carefully reviewed and selected from 36 submissions. The papers are organized in the two topical sections: Applications of Analytics in Business and Machine Learning & Deep Learning and Text Analytics.

This volume LNCS 14396 constitutes the refereed proceedings of the 12th International Conference, MEDI 2023,in November 2023 ,held in Sousse, Tunisia.The 27 full papers were carefully peer reviewed and selected from 99 submissions. The Annual International Conference on Model and Data Engineering focuses on bring together researchers and practitioners and enabling them to showcase the latest advances in modelling and data management.

This book constitutes the proceedings of the 6th International Conference on Dynamics of Information Systems, DIS 2023, which took place in Prague, Czech Republic, in September 2023. The 18 full papers included in the book were carefully reviewed and selected from 43 submissions. They deal with topics ranging from theoretical, algorithmic, and practical perspectives in information systems, to offering readers valuable information, theories and techniques.

This book constitutes the Revised Selected Papers of the Second International Conference, ICAETA 2023, held in Istanbul, Turkey, during March 10¿11, 2023.The 37 full papers included in this volume were carefully reviewed and selected from 139 submissions. The topics cover a range of areas related to engineering, technology, and applications. Main themes of the conference include, but are not limited to: Data Analysis, Visualization and Applications; Artificial Intelligence, Machine Learning and Computer Vision; Computer Communication and Networks; Signal Processing and Applications; Electronic Circuits, Devices, and Photonics; Power Electronics and Energy Systems.

This book presents a new way of thinking about quantum mechanics and machine learning by merging the two. Quantum mechanics and machine learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and machine learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits¿ performance. The book begins with the introduction of programming tools and basic concepts of machine learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs. This book is essential reading for graduate students and researchers who want to develop both the requisite physics and coding knowledge to understand the rich interplay of quantum mechanics and machine learning.

Unlock the Secrets of Neural Networks in Minutes! Dive into a concise lecture covering essential concepts like weight initialization, early stopping, and the pivotal role of hidden units. Explore the magic of input and output encodings, unravel the mysteries of recurrent neural networks, and grasp the power of autoencoders, including the transformative stacked autoencoders. Elevate your understanding of neural networks in no time with this bite-sized journey into the heart of artificial intelligence!

Course design myths and concerns debunkedHave you ever dreamed of creating a course? Whether it's online, in-person, or blended, designing an effective course can seem daunting. A web of myths and concerns often holds us back, limiting our ability to share our passion and knowledge with the world. But don't worry! Today, we'll tackle these myths and concerns and take you on the exciting journey of course design.Myth #1: Course design is complex and time-consuming.Reality: Course design doesn't have to be complex. It can be broken down into small, manageable steps. There is a wealth of resources available on the internet, including templates, checklists, and even free courses, to guide you through the process. Remember, Rome wasn't built in a day, and neither should your course. Take it slow, focus on your goals, and you'll be surprised how quickly you can create a great course.Myth #2: I need to be an expert.Reality: Everyone has something to teach others, no matter how small or large. You don't need to be a top expert in the field. You can share your experiences, passions, and knowledge and provide value to your students. Be confident, tell your story, and you'll inspire and empower your students.Here are some additional tips for overcoming course design myths and concerns:· Start small. Don't try to create the perfect course right out of the gate. Start with a small, focused topic that you're passionate about. You can always expand your course later on.· Get feedback. Once you have a draft of your course, share it with others and get their feedback. This will help you identify any areas that need improvement.· Be flexible. Things don't always go according to plan. Be prepared to make changes to your course as needed.

This book constitutes the proceedings of the First International Conference, CINS 2023, held in Dubai, United Arab Emirates, from October 18 to 20, 2023.The 11 full papers included in this volume were carefully reviewed and selected from 130 submissions. This volume discusses contemporary challenges within computing systems and the utilization of intelligent approaches to improve computing methodologies, data processing capabilities, and the application of these intelligent techniques. The book also addresses several topics pertaining to networks, including security, network data processing, networks that transcend boundaries, device heterogeneity, and advancements in networks connected to the Internet of Things, software-defined networks, cloud computing, and intelligent networks.