Title
Foundations Of Machine Learning (Adaptive Computation And Machine Learning)
Delivery time: 8-12 business days (International)
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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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 'Foundations of Machine Learning'? A: The book covers fundamental concepts in machine learning, including regression, multi-class classification, ranking, and various modern algorithms along with their theoretical foundations.
- Q: Who is the intended audience for this textbook? A: This textbook is intended for graduate students and researchers in machine learning, statistics, and related fields.
- Q: Is this book suitable for self-study? A: Yes, the book can be used as either a textbook for structured learning or as a reference for self-study, as each chapter is mostly self-contained.
- Q: What is the condition of the used book? A: The book is listed as 'Used Book in Good Condition', indicating that it may show some signs of wear but is still usable and intact.
- Q: What is the binding type of the book? A: The book is available in hardcover binding, which is durable and suitable for long-term use.
- Q: How many pages does the book have? A: The book contains a total of 412 pages.
- Q: Who are the authors of 'Foundations of Machine Learning'? A: The book is authored by Mehryar Mohri, who is known for his contributions to the field of machine learning.
- Q: When was the book published? A: The book was published on January 1, 2012.
- Q: Does the book include any supplementary materials? A: Yes, the appendix offers a concise probability review, an introduction to convex optimization, and tools for concentration bounds.
- Q: What makes this book different from other machine learning textbooks? A: This book emphasizes theoretical details and proofs, covering topics that are often overlooked in other texts, making it a comprehensive resource for both learning and reference.