Fundamentals of Machine Vision

Fundamentals of Machine Vision
Author: Harley R. Myler
Publisher: SPIE Press
Total Pages: 156
Release: 1999
Genre: Computers
ISBN: 9780819430496

This text is intended to help readers understand and construct machine vision systems that perform useful tasks, based on the state of the art. It covers fundamentals drawn from image processing and computer graphics to the methods of applied machine vision techniques. The text is useful as a short course supplement, as a self-study guide, or as a primary or supplementary text in an advanced undergraduate or graduate course.

Handbook Of Industrial Automation

Handbook Of Industrial Automation
Author: Richard Shell
Publisher: CRC Press
Total Pages: 912
Release: 2000-08-29
Genre: Business & Economics
ISBN: 9780203908587

Supplies the most essential concepts and methods necessary to capitalize on the innovations of industrial automation, including mathematical fundamentals, ergonometrics, industrial robotics, government safety regulations, and economic analyses.

Fundamentals Of Machine Learning & Artificial Intelligence

Fundamentals Of Machine Learning & Artificial Intelligence
Author: Dr. Abdul Rahiman Sheik
Publisher: Academic Guru Publishing House
Total Pages: 215
Release: 2023-07-07
Genre: Study Aids
ISBN: 8119338553

An upcoming game-changing technology that is disrupting the digital & computer technology age is artificial intelligence (AI). The whole of the information technology industry has adopted the use of machine learning & artificial algorithms in order to automate processes and provide robust outcomes. This book will familiarize you with the fundamental concepts and important phrases of the area of computer science that is seeing the most rapid expansion, as well as: An explanation of the many methods and algorithms that are utilized in machine learning, including why & how they are used as well as the tools that are necessary. Where to get data, which languages are most suited for machine learning, and what kinds of technologies are available to assist you with your task. This book provides an introduction to the foundations of contemporary artificial intelligence (AI), as well as coverage of recent developments in AI, such as Automated Planning, Information Retrieval, Intelligent Agents, Natural Language and Speech Processing, and Machine Vision. A short historical background can be found at the beginning of each chapter. This book explains, in terminology that is easy to understand, almost all of the components of artificial intelligence, including problem solving, search strategies, knowledge concepts, expert systems, and many more.

Fundamentals of Machine Learning

Fundamentals of Machine Learning
Author: Thomas Trappenberg
Publisher: Oxford University Press
Total Pages: 260
Release: 2019-11-28
Genre: Computers
ISBN: 0192563092

Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning. Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.

Understanding the Fundamentals of Machine Learning and AI for Digital Business

Understanding the Fundamentals of Machine Learning and AI for Digital Business
Author: Andy Ismail
Publisher: Asadel Publisher
Total Pages: 135
Release: 2023-06-04
Genre: Computers
ISBN:

"Understanding the Fundamentals of Machine Learning and AI for Digital Business" is a comprehensive guide that provides a solid foundation in the concepts and applications of machine learning and artificial intelligence. This book covers a wide range of topics, from the history and understanding of machine learning to its purpose and application in the digital business landscape. Starting with the basics, readers will gain a clear understanding of supervised learning, unsupervised learning, and reinforcement learning. They will explore evaluation methods such as accuracy, precision, recall, F1 score, and ROC-AUC, and learn how to assess the performance of machine learning models. The book delves into regression analysis, covering important techniques like polynomial regression, ridge regression, lasso regression, and vector regression. It also explores classification methods, including Naive Bayes, K-Nearest Neighbors (KNN), decision trees, random forest, and support vector machines. Readers will gain insights into clustering techniques like K-means, hierarchical clustering, and density-based clustering. They will also explore the fascinating world of deep learning, including convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), and natural language processing (NLP) techniques like tokenization, stemming, and lemmatization. The book provides practical exercises throughout, allowing readers to apply their knowledge and reinforce their understanding. It covers topics such as dealing with violations of assumptions, model selection and validation, and advanced regression techniques. Ethical considerations in machine learning and AI are also addressed, highlighting the importance of responsible and ethical practices in the digital business environment. With its comprehensive coverage and practical exercises, "Understanding the Fundamentals of Machine Learning and AI for Digital Business" is an essential resource for students, professionals, and anyone interested in harnessing the power of machine learning and AI in the digital era. It offers a solid foundation in theory and practical applications, equipping readers with the skills to navigate the evolving landscape of machine learning and AI and drive digital business success.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Fundamentals of Machine Learning for Predictive Data Analytics, second edition
Author: John D. Kelleher
Publisher: MIT Press
Total Pages: 853
Release: 2020-10-20
Genre: Computers
ISBN: 0262361108

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Fundamentals of Machine Learning and Its Applications

Fundamentals of Machine Learning and Its Applications
Author: Ms. Priyanka Sharma
Publisher: Academic Guru Publishing House
Total Pages: 209
Release: 2023-09-04
Genre: Study Aids
ISBN: 8119832078

Fundamentals of Machine Learning and Its Applications serve as an indispensable guide for both novices and seasoned professionals delving into the intricate realm of machine learning. Authored with precision and clarity, this book navigates the multifaceted landscape of machine learning, unravelling its core concepts, methodologies, and practical implementations. The book adeptly commences by elucidating the foundational principles that underpin machine learning, progressively leading the reader through a comprehensive journey of understanding. It demystifies intricate algorithms, presenting them in a digestible manner, while also shedding light on the mathematical and statistical underpinnings that govern their functioning. One of the distinguishing features of this literary work lies in its emphasis on real-world applications. Through illuminating case studies and examples spanning diverse domains, including image recognition, natural language processing, and recommendation systems, the book bridges the gap between theory and application. This allows readers to not only grasp theoretical nuances but also to harness this knowledge in pragmatic scenarios. In a rapidly evolving field, staying abreast of the latest trends and advancements is crucial. The book acknowledges this by incorporating a section dedicated to contemporary developments, such as deep learning and neural networks. By doing so, it equips learners with insights that reflect the current state of the discipline. Fundamentals of Machine Learning and Its Applications stand as an indispensable resource, fostering a holistic comprehension of machine learning's bedrock principles and its diverse real-world implementations. It caters to eager learners aiming to fortify their expertise in this transformative domain.

Fundamentals of Machine Learning and Deep Learning in Medicine

Fundamentals of Machine Learning and Deep Learning in Medicine
Author: Reza Borhani
Publisher: Springer Nature
Total Pages: 201
Release: 2022-11-18
Genre: Medical
ISBN: 3031195027

This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites.

Foundations of Computer Vision

Foundations of Computer Vision
Author: Antonio Torralba
Publisher: MIT Press
Total Pages: 981
Release: 2024-04-16
Genre: Computers
ISBN: 0262048973

An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances. Machine learning has revolutionized computer vision, but the methods of today have deep roots in the history of the field. Providing a much-needed modern treatment, this accessible and up-to-date textbook comprehensively introduces the foundations of computer vision while incorporating the latest deep learning advances. Taking a holistic approach that goes beyond machine learning, it addresses fundamental issues in the task of vision and the relationship of machine vision to human perception. Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process. To emphasize intuitive learning, concepts are presented in short, lucid chapters alongside extensive illustrations, questions, and examples. Written by leaders in the field and honed by a decade of classroom experience, this engaging and highly teachable book offers an essential next-generation view of computer vision. Up-to-date treatment integrates classic computer vision and deep learning Accessible approach emphasizes fundamentals and assumes little background knowledge Student-friendly presentation features extensive examples and images Proven in the classroom Instructor resources include slides, solutions, and source code