Learning Matlab
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Learning Matlab
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Author : Tobin A. Driscoll
language : en
Publisher: SIAM
Release Date : 2009-07-23
Learning Matlab written by Tobin A. Driscoll and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-23 with Mathematics categories.
A handbook for MATLAB which gives a focused approach to the software for students and professional researchers.
Matlab For Machine Learning
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Author : Giuseppe Ciaburro
language : en
Publisher:
Release Date : 2017-08-24
Matlab For Machine Learning written by Giuseppe Ciaburro and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-24 with categories.
Extract patterns and knowledge from your data in easy way using MATLABAbout This Book* Get your first steps into machine learning with the help of this easy-to-follow guide* Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB* Understand how your data works and identify hidden layers in the data with the power of machine learning.Who This Book Is ForThis book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well.What You Will Learn* Learn the introductory concepts of machine learning.* Discover different ways to transform data using SAS XPORT, import and export tools,* Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data.* Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment.* Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.* Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox.* Learn feature selection and extraction for dimensionality reduction leading to improved performance.In DetailMATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners.You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions.You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement.At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.Style and approachThe book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
Matlab Machine Learning
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Author : Michael Paluszek
language : en
Publisher: Apress
Release Date : 2016-12-29
Matlab Machine Learning written by Michael Paluszek and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-29 with Computers categories.
This book is a comprehensive guide to machine learning with worked examples in MATLAB. It starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning. The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results. Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology. The book then provides complete solutions in MATLAB for several important problems in machine learning including face identification, autonomous driving, and data classification. Full source code is provided for all of the examples and applications in the book. What you'll learn: An overview of the field of machine learning Commercial and open source packages in MATLAB How to use MATLAB for programming and building machine learning applications MATLAB graphics for machine learning Practical real world examples in MATLAB for major applications of machine learning in big data Who is this book for: The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.
Matlab For Dummies
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Author : John Paul Mueller
language : en
Publisher: John Wiley & Sons
Release Date : 2021-06-29
Matlab For Dummies written by John Paul Mueller and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-29 with Computers categories.
Go from total MATLAB newbie to plotting graphs and solving equations in a flash! MATLAB is one of the most powerful and commonly used tools in the STEM field. But did you know it doesn’t take an advanced degree or a ton of computer experience to learn it? MATLAB For Dummies is the roadmap you’ve been looking for to simplify and explain this feature-filled tool. This handy reference walks you through every step of the way as you learn the MATLAB language and environment inside-and-out. Starting with straightforward basics before moving on to more advanced material like Live Functions and Live Scripts, this easy-to-read guide shows you how to make your way around MATLAB with screenshots and newly updated procedures. It includes: A comprehensive introduction to installing MATLAB, using its interface, and creating and saving your first file Fully updated to include the 2020 and 2021 updates to MATLAB, with all-new screenshots and up-to-date procedures Enhanced debugging procedures and use of the Symbolic Math Toolbox Brand new instruction on working with Live Scripts and Live Functions, designing classes, creating apps, and building projects Intuitive walkthroughs for MATLAB’s advanced features, including importing and exporting data and publishing your work Perfect for STEM students and new professionals ready to master one of the most powerful tools in the fields of engineering, mathematics, and computing, MATLAB For Dummies is the simplest way to go from complete newbie to power user faster than you would have thought possible.
Foundations Of Computational Finance With Matlab
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Author : Ed McCarthy
language : en
Publisher: John Wiley & Sons
Release Date : 2018-05-29
Foundations Of Computational Finance With Matlab written by Ed McCarthy and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-29 with Business & Economics categories.
Graduate from Excel to MATLAB® to keep up with the evolution of finance data Foundations of Computational Finance with MATLAB® is an introductory text for both finance professionals looking to branch out from the spreadsheet, and for programmers who wish to learn more about finance. As financial data grows in volume and complexity, its very nature has changed to the extent that traditional financial calculators and spreadsheet programs are simply no longer enough. Today’s analysts need more powerful data solutions with more customization and visualization capabilities, and MATLAB provides all of this and more in an easy-to-learn skillset. This book walks you through the basics, and then shows you how to stretch your new skills to create customized solutions. Part I demonstrates MATLAB’s capabilities as they apply to traditional finance concepts, and PART II shows you how to create interactive and reusable code, link with external data sources, communicate graphically, and more. Master MATLAB’s basic operations including matrices, arrays, and flexible data structures Learn how to build your own customized solutions when the built-ins just won’t do Learn how to handle financial data and industry-specific variables including risk and uncertainty Adopt more accurate modeling practices for portfolios, options, time series, and more MATLAB is an integrated development environment that includes everything you need in one well-designed user interface. Available Toolboxes provide tested algorithms that save you hours of code, and the skills you learn using MATLAB make it easier to learn additional languages if you choose to do so. Financial firms are catching up to universities in MATLAB usage, so this is skill set that will follow you throughout your career. When you’re ready to step into the new age of finance, Foundations of Computational Finance with MATLAB provides the expert instruction you need to get started quickly.
Introduction To Statistical Machine Learning
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Author : Masashi Sugiyama
language : en
Publisher: Morgan Kaufmann
Release Date : 2015-10-31
Introduction To Statistical Machine Learning written by Masashi Sugiyama and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-31 with Mathematics categories.
Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks. - Provides the necessary background material to understand machine learning such as statistics, probability, linear algebra, and calculus - Complete coverage of the generative approach to statistical pattern recognition and the discriminative approach to statistical machine learning - Includes MATLAB/Octave programs so that readers can test the algorithms numerically and acquire both mathematical and practical skills in a wide range of data analysis tasks - Discusses a wide range of applications in machine learning and statistics and provides examples drawn from image processing, speech processing, natural language processing, robot control, as well as biology, medicine, astronomy, physics, and materials
Teaching Electromagnetics
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Author : Krishnasamy T. Selvan
language : en
Publisher: CRC Press
Release Date : 2021-06-18
Teaching Electromagnetics written by Krishnasamy T. Selvan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-18 with Technology & Engineering categories.
Teaching Electromagnetics: Innovative Approaches and Pedagogical Strategies is a guide for educators addressing course content and pedagogical methods primarily at the undergraduate level in electromagnetic theory and its applications. Topics include teaching methods, lab experiences and hands-on learning, and course structures that help teachers respond effectively to trends in learning styles and evolving engineering curricula. The book grapples with issues related to the recent worldwide shift to remote teaching. Each chapter begins with a high-level consideration of the topic, reviews previous work and publications, and gives the reader a broad picture of the topic before delving into details. Chapters include specific guidance for those who want to implement the methods and assessment results and evaluation of the effectiveness of the methods. Respecting the limited time available to the average teacher to try new methods, the chapters focus on why an instructor should adopt the methods proposed in it. Topics include virtual laboratories, computer-assisted learning, and MATLAB® tools. The authors also review flipped classrooms and online teaching methods that support remote teaching and learning. The end result should be an impact on the reader represented by improvements to his or her practical teaching methods and curricular approach to electromagnetics education. The book is intended for electrical engineering professors, students, lab instructors, and practicing engineers with an interest in teaching and learning. In summary, this book: Surveys methods and tools for teaching the foundations of wireless communications and electromagnetic theory Presents practical experience and best practices for topical coverage, course sequencing, and content Covers virtual laboratories, computer-assisted learning, and MATLAB tools Reviews flipped classroom and online teaching methods that support remote teaching and learning Helps instructors in RF systems, field theory, and wireless communications bring their teaching practice up to date Dr. Krishnasamy T. Selvan is Professor in the Department of Electronics & Communication Engineering, SSN College of Engineering, since June 2012. Dr. Karl F. Warnick is Professor in the Department of Electrical and Computer Engineering at BYU.
Randomness And Elements Of Decision Theory Applied To Signals
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Author : Monica Borda
language : en
Publisher: Springer Nature
Release Date : 2021-12-10
Randomness And Elements Of Decision Theory Applied To Signals written by Monica Borda and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-10 with Computers categories.
This book offers an overview on the main modern important topics in random variables, random processes, and decision theory for solving real-world problems. After an introduction to concepts of statistics and signals, the book introduces many essential applications to signal processing like denoising, texture classification, histogram equalization, deep learning, or feature extraction. The book uses MATLAB algorithms to demonstrate the implementation of the theory to real systems. This makes the contents of the book relevant to students and professionals who need a quick introduction but practical introduction how to deal with random signals and processes
Cyber Physical Systems
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Author : Uzzal Sharma
language : en
Publisher: John Wiley & Sons
Release Date : 2022-06-28
Cyber Physical Systems written by Uzzal Sharma and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-28 with Computers categories.
CYBER-PHYSICAL SYSTEMS The 13 chapters in this book cover the various aspects associated with Cyber-Physical Systems (CPS) such as algorithms, application areas, and the improvement of existing technology such as machine learning, big data and robotics. Cyber-Physical Systems (CPS) is the interconnection of the virtual or cyber and the physical system. It is realized by combining three well-known technologies, namely “Embedded Systems,” “Sensors and Actuators,” and “Network and Communication Systems.” These technologies combine to form a system known as CPS. In CPS, the physical process and information processing are so tightly connected that it is hard to distinguish the individual contribution of each process from the output. Some exciting innovations such as autonomous cars, quadcopter, spaceships, sophisticated medical devices fall under CPS. The scope of CPS is tremendous. In CPS, one sees the applications of various emerging technologies such as artificial intelligence (AI), Internet of Things (IoT), machine learning (ML), deep learning (DL), big data (BD), robotics, quantum technology, etc. In almost all sectors, whether it is education, health, human resource development, skill improvement, startup strategy, etc., one sees an enhancement in the quality of output because of the emergence of CPS into the field. Audience Researchers in Information technology, artificial intelligence, robotics, electronics and electrical engineering.
Intelligent Connectivity
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Author : Abdulrahman Yarali
language : en
Publisher: John Wiley & Sons
Release Date : 2021-09-28
Intelligent Connectivity written by Abdulrahman Yarali and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-28 with Computers categories.
INTELLIGENT CONNECTIVITY AI, IOT, AND 5G Explore the economics and technology of AI, IOT, and 5G integration Intelligent Connectivity: AI, IoT, and 5G delivers a comprehensive technological and economic analysis of intelligent connectivity and the integration of artificial intelligence, Internet of Things (IoT), and 5G. It covers a broad range of topics, including Machine-to-Machine (M2M) architectures, edge computing, cybersecurity, privacy, risk management, IoT architectures, and more. The book offers readers robust statistical data in the form of tables, schematic diagrams, and figures that provide a clear understanding of the topic, along with real-world examples of applications and services of intelligent connectivity in different sectors of the economy. Intelligent Connectivity describes key aspects of the digital transformation coming with the 4th industrial revolution that will touch on industries as disparate as transportation, education, healthcare, logistics, entertainment, security, and manufacturing. Readers will also get access to: A thorough introduction to technology adoption and emerging trends in technology, including business trends and disruptive new applications Comprehensive explorations of telecommunications transformation and intelligent connectivity, including learning algorithms, machine learning, and deep learning Practical discussions of the Internet of Things, including its potential for disruption and future trends for technological development In-depth examinations of 5G wireless technology, including discussions of the first five generations of wireless tech Ideal for telecom and information technology managers, directors, and engineers, Intelligent Connectivity: AI, IoT, and 5G is also an indispensable resource for senior undergraduate and graduate students in telecom and computer science programs.