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Understanding Machine Learning
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
414 pages, Hardcover
First published April 30, 2014
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Displaying 1 - 8 of 8 reviews
December 18, 2022
این کتاب خوبیه
اگر همزمان کلاس‌ها� شای بن دیوید تو یوتیوب رو هم ببینید خیلی بهتره.
همین کتاب رو تدریس می‌کن�
اگر همزمان کلاس‌ها� شای بن دیوید تو یوتیوب رو هم ببینید خیلی بهتره.
همین کتاب رو تدریس می‌کن�
December 19, 2018
Most of the core theories in ML are covered. It is suitable for CS enthusiastic people.
January 2, 2022
Definitely my machine learning bible. Far too many fields in this blossoming field have too much emphasis on the application and construction of algorithms and very few actually go in depth in the theory behind what makes a ML algorithm work. I would say every ML researcher should read this book once or at least have it as a reference.
April 3, 2017
Excellent introduction to Machine Learning Theory. I have written a Persian review:
July 8, 2021
مخاطب
کتاب درک یادگیری ماشین به مباحث نظری یادگیری ماشین می‌پرداز�. مخاطبین این کتاب افرادی هستند که علاقمند به درک مباحث ریاضی پشت الگوریتم‌ها� یادگیری هستند.
محتوا
کتاب ابتدا با مفهوم یادگیری آغاز می‌کن� و اینکه چطور می‌توا� آن را به صورت احتمالی و محاسباتی بیان کرد. به همین منظور مدلی به نام Probably Approximately Correct یا همان PAC معرفی می‌کن�. در باقی فصل‌ها� کتاب الگوریتم‌ها� یادگیری ماشین مانند درخت تصمیم، نزدیک‌تری� همسایه و ... را از منظر مدل PAC بررسی می‌کن�.
یکی از بخش‌ها� جالب کتاب قضیه ناهار مجانی و اثبات آن بود و نتیجه‌ا� که از آن می‌توا� گرفت (حداقل در دنیای یادگیری ماشین):
There ain't no such thing as a free lunch
نویسنده دوم کتاب (بن دیوید) ویدیوها� تدریس خود در دانشگاه واترلو را قرار داده است که از این می‌توانی� مشاهده کنید. البته ویدیوها فقط نیمه اول کتاب را در بر می‌گیر�.
جمع‌بند�
کتاب متن سلیسی دارد و خواندنش علی رغم ریاضیات سنگین آن برای بنده بسیار لذت‌بخ� بود. اگر می‌خواهی� درک عمیق‌تر� نسبت به یادگیری ماشین داشته باشید، این کتاب فوق‌العاد� است.
January 13, 2021
Didn't get to finish all the chapters of the book in details but did read first few chapters minutely. The approach of the book was beneficial for me to grasp the concept of PAC and its implication in learning theory.
Read
April 3, 2021Used for a class as supplementary material. Think it would have been too dense without the class, but I thought it did a great job of conveying the theory behind ML in a way that was not overly abstract.
July 10, 2024
very good fundamentals
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