machine learning and fault detection books. Contribute to maidamai0/ML- FDbooks development by creating an account on GitHub. Email; Facebook; Twitter; Linked In; Reddit; CiteULike. View Table of Contents for An Elementary Introduction to Statistical Learning Theory. An Elementary Introduction to Statistical Learning Theory. SANJEEV KULKARNI. Department of Electrical Engineering. School of Engineering and Applied.
An Introduction to Statistical. Learning. Gareth James. Daniela Witten. Trevor Hastie Statistical learning refers to a set of tools for modeling and understanding complex We expect that the reader will have had at least one elementary. This tutorial intro- The main goal of statistical learning theory is to provide a framework for .. The elementary material from probability theory that is needed. An_Elementary Intro to Statistical Learning Theory - Download as PDF File .pdf) or view presentation slides online. Statistical Learning.
A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study.