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OverviewThere is a wealth of literature and books available to engineers starting to understand what machine learning is and how it can be used in their everyday work. This presents the problem of where the engineer should start. The answer is often “for a general, but slightly outdated introduction, read this book; for a detailed survey of methods based on probabilistic models, check this reference; to learn about statistical learning, this text is useful” and so on. This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment, encompassing recent developments and pointers to the literature for further study. A Brief Introduction to Machine Learning for Engineers is the entry point to machine learning for students, practitioners, and researchers with an engineering background in probability and linear algebra. Full Product DetailsAuthor: Osvaldo SimeonePublisher: now publishers Inc Imprint: now publishers Inc Weight: 0.359kg ISBN: 9781680834727ISBN 10: 168083472 Pages: 250 Publication Date: 14 August 2018 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsI. Basics 1. Introduction 2. A Gentle Introduction through Linear Regression 3. Probabilistic Models for Learning II. Supervised Learning 4. Classification 5. Statistical Learning Theory III. Unsupervised Learning 6. Unsupervised Learning IV. Advanced Modelling and Inference 7. Probabilistic Graphical Models 8. Approximate Inference and Learning V. Conclusions 9. Concluding Remarks Appendices Acknowledgements ReferencesReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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