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Introduction To Machine Learning (Adaptive Computation And Machine Learning),New
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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, Semiparametric, 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.
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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has six hundred thirteen pages. It's a comprehensive resource for those studying machine learning.
- Q: What is the size of this book? A: The dimensions of this book are nine point twenty-six inches in length, eight point fifteen inches in width, and one point twenty-two inches in height.
- Q: What type of binding does this book have? A: This book is bound in hardcover. This ensures durability and longevity for frequent use.
- Q: Who is the author of this book? A: The author of this book is Ethem Alpaydin. He is well-regarded in the field of machine learning.
- Q: What is the main subject of this book? A: This book focuses on machine learning. It covers a broad range of topics suited for both beginners and advanced learners.
- Q: Is this book suitable for beginners? A: Yes, this book includes support for beginners. It offers selected solutions for exercises and additional example data sets.
- Q: Can I use this book for graduate-level studies? A: Yes, this book is appropriate for graduate students. It covers advanced topics that are essential for higher education.
- Q: What learning algorithms are covered in this book? A: This book explains various learning algorithms, including supervised learning, reinforcement learning, and Bayesian methods.
- Q: Are there practical examples included in this book? A: Yes, the book includes practical examples and code available online. This aids in understanding the application of concepts.
- Q: How should I care for this book? A: To care for this book, keep it in a dry environment and avoid exposure to direct sunlight. This will help preserve its condition.
- Q: Is this book safe for children? A: No, this book is primarily aimed at college students and professionals. It contains advanced topics not suitable for children.
- Q: What if my book arrives damaged? A: If your book arrives damaged, you should contact the seller for a return or exchange policy. Most sellers will assist you with this issue.
- Q: Are there any specific storage recommendations for this book? A: Store this book upright on a shelf to prevent warping. Avoid stacking heavy items on top of it to maintain its shape.
- Q: What sets this book apart from other machine learning books? A: This book covers a broad array of topics not usually included in introductory texts, making it a comprehensive resource.
- Q: Is there a warranty or guarantee for this book? A: Typically, books do not come with a warranty, but check the seller's return policy for any guarantees regarding condition.
- Q: Does this book cover deep learning? A: Yes, this book includes discussions on deep learning, specifically in multilayered perceptrons. This is crucial for modern machine learning applications.