Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.
New! Introducing the tech.book(store), a hub for Software Developers and Architects, Networking Administrators, TPMs, and other technology professionals to find highly-rated and highly-relevant career resources. Shop books on programming and big data, or read this week's blog posts by authors and thought-leaders in the tech industry. > Shop now |
Book Description
Product Details
Would you like to update product info or give feedback on images? |
More About the Authors
Discover books, learn about writers, read author blogs, and more.
Customer Reviews
Most Helpful Customer Reviews
60 of 64 people found the following review helpful
By vb
Format:Hardcover
TLDR Summary: If Machine Learning is like Mechanics, "Learning from Data" teaches you Newton's Laws!
---------------------------------
Machine Learning (ML), Data Mining (DM), Predictive Modeling, Big Data, Statistical Inference, Pattern Recognition, Regression, Classification: by whichever name you call it, you will find hundreds of books by the same name, and in theoretical as well as applied avatars. The applied ones tend to be books based on ML/DM programming libraries such as R, Weka (Java), and SciPy/NumPy (Python) and really are not meant to teach you the underlying foundations but I digress too soon.
I possess the standard three introductory texts in ML: Pattern Classification (Duda, Hart, Stork) , Pattern Recognition (Bishop) and Machine Learning (Mitchell). In addition, I have read portions of Statistical Learning (Hastie), Machine Learning (Alpaydin), Support Vector Machines (Cristianini) and several other allied ML texts in natural language processing, convex optimization etc.
In spite of being considered the classic introductory texts in ML, all these books failed in the task of making me understand what I was doing as I was practicing ML. Try as I might, I could never read through more than a few tens of pages of the afore mentioned books. And what little I read, could not be retained by my feeble brain for too long.
But where all these texts failed, "Learning From Data" (LFD) succeeds.
First an analogy:
It is all fine to arm someone with equations of cantilever beams and have them build houses, but clearly we don't want a civil engineer who doesn't understand Newton's laws to build our own house. Most well known books in ML read to me like course readers of advanced Mechanics courses stitched together. LFD on the other hand feels like a book on Newton's Laws and Applications.
Writing Style:
The book serves as the reading counterpart to a set of eighteen one-hour video lectures that was presented in a course by the first author Yaser at Caltech. The book reads almost like a transcription of the lectures. The authors are always addressing you and manage to convey the feeling that they are holding your hand and actively helping you to learn how to walk. I found the style very engaging. Once I started reading the book, it did not take any special effort for me to finish it (which was the difficulty with the other classic ML books).
The videos are freely available online(Google for 'learning from data caltech course'). I strongly encourage the readers of this book to first watch each video and then read the corresponding chapters of the book.
Content:
There are five central themes underlying the organization and presentation of topics in this book:
1. What is Learning?
2. Is Learning possible?
3. How to Learn?
4. How to Learn well?
5. Take Home Lessons.
The authors follow a style of gradual expansion from simple to complex concepts throughout the book. E.g. Under the topic of "Is Learning feasible", they first derive a probability on the upper bound of the out-of-sample error using a thought experiment and the Hoeffding's Inequality. Then they reason that if one of the components of this probability is polynomial in the number of training examples, the error can be bounded. Finally they introduce the VC dimension and prove that in cases where it is finite, learning is truly feasible.
Throughout the book, the authors provide plenty of real life application scenarios and experimentally generated examples to illustrate the theory. I found the theory when put to practice (even if in a toy example) very useful, particularly when visualized through the various graphs. There are several gems scattered around the book in the form of subtle things that can be overlooked even by a smart person (such as inadvertent data snooping) and rules-of-thumb for practical applications.
The authors have clearly had to make some choices about what to focus on and what to omit. For example, the book has no mention of Bayesian Decision Theory or Naive Bayes classification. This appears shocking upon first glance since Naive Bayes is often the first learning algorithm taught in an introductory course on pattern recognition. But after going though Yaser's book/course such omissions appears to be a virtue. It is not the focus of this book to teach you everything ML. If this is what you are looking for, LFD is not for you; Kevin's Murphy's forthcoming text appears promising. LFD however, gives you enough of a foundation that should you wish to educate yourself on advance topics like bootstrap aggregation, probabilistic graphical models, or ensemble-learning, you are sufficiently prepared.
The icing on the cake is the forum provided by the authors to discuss the book (and the lectures). Yaser has personally answered all of my questions, sometimes at 3AM, Pasadena time!
Final note on book quality:
The color printing, binding and paper quality are all excellent. The authors could have paid more attention to detail to some portions of the book (such as using high-contrast, colorblind-friendly colors in the illustrations) but honestly, this is just me being extremely an*l. The hardbound book at this low price of approximately $30 is pure value for money. Wide dissemination of the book contents appears to be a clear motivation.
PS: If the authors are reading this, they should look up "Ishihara test plates" and compare that with the illustration of red-green marbles on page 22 etc.
---------------------------------
Machine Learning (ML), Data Mining (DM), Predictive Modeling, Big Data, Statistical Inference, Pattern Recognition, Regression, Classification: by whichever name you call it, you will find hundreds of books by the same name, and in theoretical as well as applied avatars. The applied ones tend to be books based on ML/DM programming libraries such as R, Weka (Java), and SciPy/NumPy (Python) and really are not meant to teach you the underlying foundations but I digress too soon.
I possess the standard three introductory texts in ML: Pattern Classification (Duda, Hart, Stork) , Pattern Recognition (Bishop) and Machine Learning (Mitchell). In addition, I have read portions of Statistical Learning (Hastie), Machine Learning (Alpaydin), Support Vector Machines (Cristianini) and several other allied ML texts in natural language processing, convex optimization etc.
In spite of being considered the classic introductory texts in ML, all these books failed in the task of making me understand what I was doing as I was practicing ML. Try as I might, I could never read through more than a few tens of pages of the afore mentioned books. And what little I read, could not be retained by my feeble brain for too long.
But where all these texts failed, "Learning From Data" (LFD) succeeds.
First an analogy:
It is all fine to arm someone with equations of cantilever beams and have them build houses, but clearly we don't want a civil engineer who doesn't understand Newton's laws to build our own house. Most well known books in ML read to me like course readers of advanced Mechanics courses stitched together. LFD on the other hand feels like a book on Newton's Laws and Applications.
Writing Style:
The book serves as the reading counterpart to a set of eighteen one-hour video lectures that was presented in a course by the first author Yaser at Caltech. The book reads almost like a transcription of the lectures. The authors are always addressing you and manage to convey the feeling that they are holding your hand and actively helping you to learn how to walk. I found the style very engaging. Once I started reading the book, it did not take any special effort for me to finish it (which was the difficulty with the other classic ML books).
The videos are freely available online(Google for 'learning from data caltech course'). I strongly encourage the readers of this book to first watch each video and then read the corresponding chapters of the book.
Content:
There are five central themes underlying the organization and presentation of topics in this book:
1. What is Learning?
2. Is Learning possible?
3. How to Learn?
4. How to Learn well?
5. Take Home Lessons.
The authors follow a style of gradual expansion from simple to complex concepts throughout the book. E.g. Under the topic of "Is Learning feasible", they first derive a probability on the upper bound of the out-of-sample error using a thought experiment and the Hoeffding's Inequality. Then they reason that if one of the components of this probability is polynomial in the number of training examples, the error can be bounded. Finally they introduce the VC dimension and prove that in cases where it is finite, learning is truly feasible.
Throughout the book, the authors provide plenty of real life application scenarios and experimentally generated examples to illustrate the theory. I found the theory when put to practice (even if in a toy example) very useful, particularly when visualized through the various graphs. There are several gems scattered around the book in the form of subtle things that can be overlooked even by a smart person (such as inadvertent data snooping) and rules-of-thumb for practical applications.
The authors have clearly had to make some choices about what to focus on and what to omit. For example, the book has no mention of Bayesian Decision Theory or Naive Bayes classification. This appears shocking upon first glance since Naive Bayes is often the first learning algorithm taught in an introductory course on pattern recognition. But after going though Yaser's book/course such omissions appears to be a virtue. It is not the focus of this book to teach you everything ML. If this is what you are looking for, LFD is not for you; Kevin's Murphy's forthcoming text appears promising. LFD however, gives you enough of a foundation that should you wish to educate yourself on advance topics like bootstrap aggregation, probabilistic graphical models, or ensemble-learning, you are sufficiently prepared.
The icing on the cake is the forum provided by the authors to discuss the book (and the lectures). Yaser has personally answered all of my questions, sometimes at 3AM, Pasadena time!
Final note on book quality:
The color printing, binding and paper quality are all excellent. The authors could have paid more attention to detail to some portions of the book (such as using high-contrast, colorblind-friendly colors in the illustrations) but honestly, this is just me being extremely an*l. The hardbound book at this low price of approximately $30 is pure value for money. Wide dissemination of the book contents appears to be a clear motivation.
PS: If the authors are reading this, they should look up "Ishihara test plates" and compare that with the illustration of red-green marbles on page 22 etc.
28 of 31 people found the following review helpful
Format:Hardcover
When I first read this book I had high expectations, I was lucky to have Yaser as one of my best teachers at Caltech during my PhD and I still remember his energy and passion.
Well, the book does translate into printed words the passion for "really understanding a subject" that he and his co-authors share in their professional life.
By "really understanding" they mean understanding the foundations of learning from data but also going beyond abstractions to give flesh and blood to ideas. Motivation always anticipates the definition of concepts, and after concepts are formulated, the discussion continues to "really understand" the meaning of equations and theorems.
The topics contained in the book are limited ("a short course, not a hurried course") because of an explicit choice: if one understands the meaning, implications, and pitfalls of learning from data in simple scenarios (like linear models) he will then be equipped to venture into more complicated territories.
The best chapter in my (biased) opinion is the last one about "Three learning principles", ten pages combining principles and real-world examples in a breathtaking sequence: Occam's razor, sampling bias, and data snooping. Mastering these ten pages will protect you from the most common pitfalls, already encountered in failing to predict presidential election results or stock market performance. After this, you will never "torture your data long enough, until they confess". A "must-read" book for students entering this exciting area but also for serious users of machine learning in business scenarios.
Well, the book does translate into printed words the passion for "really understanding a subject" that he and his co-authors share in their professional life.
By "really understanding" they mean understanding the foundations of learning from data but also going beyond abstractions to give flesh and blood to ideas. Motivation always anticipates the definition of concepts, and after concepts are formulated, the discussion continues to "really understand" the meaning of equations and theorems.
The topics contained in the book are limited ("a short course, not a hurried course") because of an explicit choice: if one understands the meaning, implications, and pitfalls of learning from data in simple scenarios (like linear models) he will then be equipped to venture into more complicated territories.
The best chapter in my (biased) opinion is the last one about "Three learning principles", ten pages combining principles and real-world examples in a breathtaking sequence: Occam's razor, sampling bias, and data snooping. Mastering these ten pages will protect you from the most common pitfalls, already encountered in failing to predict presidential election results or stock market performance. After this, you will never "torture your data long enough, until they confess". A "must-read" book for students entering this exciting area but also for serious users of machine learning in business scenarios.
19 of 20 people found the following review helpful
Format:Hardcover
For beginners:
A. For somewhat theoretical approach to machine learning
1. If you have less than a month to study it: Read this book.
2. If you have a semester: Read this book along with lecture series by Yaser's on youtube.
B. For more applied approach to machine learning
1. If you have semester: Go through Andrew Ng's lecture series
For intermediate to advanced:
1. If you have a semester: Read "Machine Learning - A Probabilistic Perspective" by Kevin Murphy (expensive but good reference book).
Other classic machine learning textbooks, if you have more time:
1. PRML - Bishop (The first book I read on Machine Learning. Very accessible. More detailed than Yasir's book, but less than Kevin's book)
2. Nature of Statistical Machine Learning - Vapnik (One of the pioneer in this field. Extremely theoretical approach)
3. Elements of Statistical Learning - Hastie et al (free pdf copy available)
A. For somewhat theoretical approach to machine learning
1. If you have less than a month to study it: Read this book.
2. If you have a semester: Read this book along with lecture series by Yaser's on youtube.
B. For more applied approach to machine learning
1. If you have semester: Go through Andrew Ng's lecture series
For intermediate to advanced:
1. If you have a semester: Read "Machine Learning - A Probabilistic Perspective" by Kevin Murphy (expensive but good reference book).
Other classic machine learning textbooks, if you have more time:
1. PRML - Bishop (The first book I read on Machine Learning. Very accessible. More detailed than Yasir's book, but less than Kevin's book)
2. Nature of Statistical Machine Learning - Vapnik (One of the pioneer in this field. Extremely theoretical approach)
3. Elements of Statistical Learning - Hastie et al (free pdf copy available)
Most Recent Customer Reviews
5.0 out of 5 stars
Great book for understanding the concepts
This is a great book if you want to get started with machine learning. This is not a book where you will find lots and lots of methods used or not used in practice (like Elements... Read more
Published 3 days ago by Shishir Pandey
5.0 out of 5 stars
Combine this with Prof. Abu-Mostafa lectures on YouTube
Prof. Abu-Mostafa is an amazing teacher and I must recommend this book highly.
This book is possibly the best "short" introduction to the subject. Read more
This book is possibly the best "short" introduction to the subject. Read more
Published 5 days ago by Sidharth Kshatriya
5.0 out of 5 stars
Awesome book for Machine Learning
Learning from Data is an excellent book for Machine Learning. This book along with the Caltech lectures by Prof. Read more
Published 10 days ago by tr293442h
5.0 out of 5 stars
Very didactic
Using this book and the online lectures it's a good way to start in Machine Learning, clear and concise, very recommended.
Published 15 days ago by GERMAN ALFARO
4.0 out of 5 stars
In line with caltechs online course (as expected!)
The book accompanies Caltech's online course of the same name https://telecourse.caltech.edu/index.php
Pretty much in line with the online lectures. Read more
Pretty much in line with the online lectures. Read more
Published 23 days ago by OXOlent_waste_of_money
5.0 out of 5 stars
The Best introduction to machine learning I came across
This books stresses less on theory (which is not problematic for a novice because other books fill that gap) but helps you have a real grasp of knowledge of what machine learning... Read more
Published 1 month ago by Mohammed Ahnouch
5.0 out of 5 stars
Great book and great companion for MOOC class
Great book on machine learning. Goes great with the CalTech MOOC course ([...]), which is starting up again the first of April. Read more
Published 1 month ago by cramp10
5.0 out of 5 stars
Good book for machine learning concepts
This book has basic build blocks for machine learning.
Chapter videos are free online in YouTube.
Easy to understand concepts and examples. Read more
Chapter videos are free online in YouTube.
Easy to understand concepts and examples. Read more
Published 2 months ago by ink
5.0 out of 5 stars
Great book. Fun to read
This is a great book, fun to read. I found particularly helpful having the book and watching the videos, because the contents are well synchronized.
Published 2 months ago by Juan Vargas
5.0 out of 5 stars
Outstanding intro the machine learning
I wish all textbooks were this thorough and well written. The book helps you master key concepts quickly and logically. This is a great place to start studying machine learning.
Published 3 months ago by LanternRouge
What Other Items Do Customers Buy After Viewing This Item?
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
Listmania!
|
|
