The goal of machine learning is to program computers to use example data or past experience to solve a given problem. This is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts.The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing.Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods

zlib/no-category/Ethem Alpaydin/Introduction to Machine Learning, third edition_28200524.pdf

Introduction to Machine Learning (Adaptive Computation and Machine Learning series)

Ethem Alpaydin; M.I.T. Press

MIT Press IEEE Xplore

AAAI Press

Adaptive computation and machine learning, Third edition, Cambridge Massachusetts [Piscataqay New Jersey, 2014

Adaptive computation and machine learning, 3rd ed, Cambridge (Massachusetts), 2014

MIT Press, Cambridge, Massachusetts, 2014

United States, United States of America

Aug 22, 2014

Source title: Introduction to Machine Learning (Adaptive Computation and Machine Learning series)

The goal of machine learning is to program computers to use example data or pas experience to solve a given problem. Many successful applications of machine learning exist already including systems that analyze past sales data to predict customer behavior, optimize robot behavio so that a task can be completed using minimum resources, and extract knowledge from bioinformatic data. Introduction to Machine Learning is a comprehensive textbook on th subject, covering a broad array of topics not usually included in introductory machine learnin texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning kernel machines; graphical models; Bayesian estimation; and statistica testing Machine learning is rapidly becoming a skill that computer scienc students must master before graduation. The third edition of Introduction to Machin Learning reflects this shift, with added support for beginners, including selecte solutions for exercises and additional example data sets (with code available online). Othe substantial changes include discussions of outlier detection; ranking algorithms for perceptrons an support vector machines; matrix decomposition and spectral methods; distance estimation; new kerne algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesia methods. All learning algorithms are explained so that students can easily move from the equation in the book to a computer program. The book can be used by both advanced undergraduates and graduat students. It will also be of interest to professionals who are concerned with the application o machine learning methods

2024-03-30