Introduction to Machine Learning, fourth edition
Introduction to Machine Learning, fourth edition is backordered and will ship as soon as it is back in stock.
Couldn't load pickup availability
Genuine Products Guarantee
Genuine Products Guarantee
We guarantee 100% genuine products, and if proven otherwise, we will compensate you with 10 times the product's cost.
Delivery and Shipping
Delivery and Shipping
Products are generally ready for dispatch within 1 day and typically reach you in 3 to 5 days.
Sign up to be the first to know when it's here
Author: Alpaydin, Ethem
Brand: MIT Press
Color: Brown
Edition: 4
Binding: Hardcover
Format: Illustrated
Number Of Pages: 712
Release Date: 24-03-2020
Details: A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks.
The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.
EAN: 9780262043793
Package Dimensions: 9.3 x 8.3 x 1.7 inches
Languages: English
