Introduction to Machine Learning
Click to look inside
Introduction to Machine Learning
World of Books
At World of Books, you’ll find millions of preloved reads at great prices, from bestsellers to hidden gems. Every book you buy saves money and helps reduce waste, so you can read more for less while giving stories a second life.
The feel-good place to buy books
- Free US shipping over $15
- Buying preloved emits 41% less CO2 than new
- Millions of affordable books
- Give your books a new home - sell them back to us!
Introduction to Machine Learning by Ethem Alpaydin
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.
| SKU | Unavailable |
| ISBN 13 | 9780262043793 |
| ISBN 10 | 0262043793 |
| Title | Introduction to Machine Learning |
| Author | Ethem Alpaydin |
| Series | Adaptive Computation And Machine Learning Series |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | MIT Press Ltd |
| Year published | 2020-03-24 |
| Number of pages | 712 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |