Title
Introduction to Machine Learning, fourth edition (Adaptive Computation and Machine Learning series),New
Delivery time: 8-12 business days (International)
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 selfdriving 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, tSNE. New appendixes offer background material on linear algebra and optimization. Endofchapter 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.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What topics are covered in 'Introduction to Machine Learning, fourth edition'? A: This textbook covers a wide range of topics including supervised learning, Bayesian decision theory, parametric and nonparametric methods, reinforcement learning, kernel machines, and deep learning techniques.
- Q: Who is the author of this textbook? A: The author of 'Introduction to Machine Learning, fourth edition' is Ethem Alpaydin.
- Q: Is this book suitable for beginners in machine learning? A: While it is comprehensive, the book can serve as a reference for advanced undergraduate and graduate students, making it suitable for those with some prior knowledge in machine learning.
- Q: What is new in the fourth edition of this book? A: The fourth edition includes new chapters on deep learning, reinforcement learning, and updates on dimensionality reduction techniques like t-SNE, as well as new exercises for practical application.
- Q: What is the binding type of this book? A: The book is available in hardcover binding, providing durability and a professional appearance.
- Q: How many pages does this textbook have? A: The textbook consists of 712 pages, making it a comprehensive resource on machine learning.
- Q: When was 'Introduction to Machine Learning, fourth edition' published? A: This edition was published on March 24, 2020.
- Q: Does this book include exercises for practice? A: Yes, there are end-of-chapter exercises designed to help readers apply the concepts learned throughout the book.
- Q: What kind of readers would benefit from this book? A: The book is intended for advanced undergraduate and graduate students, as well as professionals seeking a comprehensive understanding of machine learning.
- Q: Are there any supplementary materials provided in this edition? A: Yes, new appendixes offer background material on linear algebra and optimization, which are essential for understanding machine learning concepts.