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Foundations Of Machine Learning (Adaptive Computation And Machine Learning)
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Fundamental Topics In Machine Learning Are Presented Along With Theoretical And Conceptual Tools For The Discussion And Proof Of Algorithms.This Graduatelevel Textbook Introduces Fundamental Concepts And Methods In Machine Learning. It Describes Several Important Modern Algorithms, Provides The Theoretical Underpinnings Of These Algorithms, And Illustrates Key Aspects For Their Application. The Authors Aim To Present Novel Theoretical Tools And Concepts While Giving Concise Proofs Even For Relatively Advanced Topics.Foundations Of Machine Learning Fills The Need For A General Textbook That Also Offers Theoretical Details And An Emphasis On Proofs. Certain Topics That Are Often Treated With Insufficient Attention Are Discussed In More Detail Here; For Example, Entire Chapters Are Devoted To Regression, Multiclass Classification, And Ranking. The First Three Chapters Lay The Theoretical Foundation For What Follows, But Each Remaining Chapter Is Mostly Selfcontained. The Appendix Offers A Concise Probability Review, A Short Introduction To Convex Optimization, Tools For Concentration Bounds, And Several Basic Properties Of Matrices And Norms Used In The Book.The Book Is Intended For Graduate Students And Researchers In Machine Learning, Statistics, And Related Areas; It Can Be Used Either As A Textbook Or As A Reference Text For A Research Seminar.
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- Q: What is the page count of this book? A: This book contains four hundred twelve pages. It's designed for graduate students and researchers in machine learning and statistics.
- Q: What is the binding type of this book? A: The book is hardcover. This durable binding provides a professional look and longevity for frequent use.
- Q: What are the dimensions of this book? A: The book measures nine point one inches in length, seven point one inches in width, and one point one inches in height. These dimensions make it portable yet substantial for study.
- Q: How do I use this book for studying? A: You can use this book as a primary textbook or as a reference text for research seminars. It is structured to allow for both sequential and independent chapter reading.
- Q: Is this book suitable for beginners in machine learning? A: No, this book is intended for graduate students and researchers. It covers advanced topics that require a foundational understanding of machine learning concepts.
- Q: Can I use this book for self-study? A: Yes, this book can be used for self-study. Each chapter is mostly self-contained, allowing readers to focus on specific topics as needed.
- Q: How should I care for this hardcover book? A: To keep this book in good condition, store it upright in a dry place. Avoid exposing it to excessive moisture or direct sunlight.
- Q: Is this book safe for all age groups? A: Yes, this book is safe for adult readers and graduate students. It covers complex topics in machine learning and is not intended for children.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, you should contact the seller for return or exchange options. Make sure to keep the original packaging for easier returns.
- Q: Does this book include any supplementary materials? A: No, this book itself does not include supplementary materials. However, it provides a comprehensive overview of machine learning topics within its chapters.
- Q: What theoretical concepts does this book cover? A: This book covers fundamental concepts in machine learning, including regression, multi-class classification, and ranking. It emphasizes theoretical underpinnings and proofs.
- Q: What are the main topics discussed in the book? A: Main topics include various algorithms in machine learning, probability review, convex optimization, and properties of matrices and norms. Each chapter offers detailed exploration.
- Q: Who is the author of this book? A: The author of this book is Mehryar Mohri. He is known for his contributions to the field of machine learning and adaptive computation.
- Q: What is the focus of the book's content? A: The focus of the book's content is on providing theoretical tools and detailed proofs of machine learning algorithms. It aims to fill gaps often left in other textbooks.
- Q: Is there a review of probability included in the book? A: Yes, the appendix includes a concise probability review. This section is useful for readers to refresh their understanding of foundational concepts.