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Learning From Data Hardcover – March 27, 2012
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This book, together with specially prepared online material freely accessible to our readers, provides a complete introduction to Machine Learning, the technology that enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Such 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. In addition, our readers are given free access to online e-Chapters that we update with the current trends in Machine Learning, such as deep learning and support vector machines. 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. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. What we have emphasized are the necessary fundamentals that give any student of learning from data a solid foundation. The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the 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.
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Top Customer Reviews
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)
I like this book because it sticks to the basics and doesn't try to over complicate things. It has an adequate treatment of theory but ultimately emphasizes practical intuition and ideas. I also really appreciate the extensive supplementary material on the website( online lectures, slides and additional homework problems).
Lastly, the price (as of January 2014) of $29 is very reasonable and was one of the main reasons I felt comfortable making it required for the class.
"Learning from Data" but it also can be used.
The author make a miracle - he explained difficult entities in elegant interesting but precise way.
Must read for everyone who want to know the profound basis of ML and not only to use code.
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.
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.
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.
Clearly the target audience is for the practicing engineer without a math degree.
Just enough of the required math is used to explain the theory along with word description of what is going on.
It avoids the convoluted gobbly-gook you see in technical papers that makes things impossible to follow
for the average engineer.
The book concisely lays things out step by step though you have to put effort to follow it.
The goal of this book is to give the reader a solid foundation for further study on machine learning.
This book is not a cookbook on how to implement machine learning.
Prof Abu-Mostafa lectures and class material are excellent and are posted on
Clearly this book is the first edition as there are additional e-chapters posted on additional topics
which will most likely be included in a future edition of this book. The book will show you how to access the additional chapters.
On the back cover it says "This is a short course, not a hurried course." And this is true.
It is short because things are concisely presented. However, it is not hurried as I needed a lot of time to digest it.
You'll need to know some multivariable calculus and linear algebra to follow along, and some programming skills to do
some of the exercises in the book.
Overall an excellent intro to machine learning.