Learning From Data: A Short Course

Learning From Data: A Short Course

Yaser S. Abu-Mostafa Malik Magdon-Ismail Hsuan-Tien Lin / Feb 26, 2020

Learning From Data A Short Course 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 fina

  • Title: Learning From Data: A Short Course
  • Author: Yaser S. Abu-Mostafa Malik Magdon-Ismail Hsuan-Tien Lin
  • ISBN: 9781600490064
  • Page: 261
  • Format: Hardcover
  • 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, weMachine 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.

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    About "Yaser S. Abu-Mostafa Malik Magdon-Ismail Hsuan-Tien Lin"

      • Yaser S. Abu-Mostafa Malik Magdon-Ismail Hsuan-Tien Lin

        Yaser S. Abu-Mostafa Malik Magdon-Ismail Hsuan-Tien Lin Is a well-known author, some of his books are a fascination for readers like in the Learning From Data: A Short Course book, this is one of the most wanted Yaser S. Abu-Mostafa Malik Magdon-Ismail Hsuan-Tien Lin author readers around the world.


    200 Comments

    1. Learning From Data does exactly what it sets out to do, and quite well at that.The book focuses on the mathematical theory of learning, why it's feasible, how well one can learn in theory, etc. Why must one learn probabilistically? Why is overfitting a very real part of life? Why can't we obsessively try every single possible hypothesis until we find a perfect match? (Oh, yes, one could formalize problems with various logical fallacies after reading this :p)As for learning algorithms, only a few [...]


    2. This is an essentially perfect little prelude to machine learning. Despite the book's short length, there is great depth in the presentation. The authors have produced a remarkably well-written and carefully presented book, with some great color illustrations as well. This is a book clearly written with the reader in mind, and I hope it soon becomes a standard primer for those embarking on deeper ML research and study.


    3. Very clear explanation, a good mix of theory and practical items. Meant for a short course, doesn't deal w/ a lot of topics. But teaches fundamentals like VC dimension, regularization, overfitting, bias and variance in great details.


    4. If you are looking for a practical handbook that contains algorithms and code that you can plug into a data set, this is not the book for you. The focus of the book is real understanding of machine learning concepts. You will know why and how things are done in a particular way. You will learn to derive algorithms and equations on your own. You would also be capable of tweaking parts of the algorithms. Make sure you understand the math really well. And also make sure you do the problem sets. Thi [...]


    5. An excellent introduction to machine learning, accessible with a small amount of university mathematics. Dr. Yaser Abu-Mostafa, one of the three authors, presents an excellent series of video lectures that follow the book very closely. The series is available from the host institution, Cal Tech: Learning from Data Video Lectures, and also on YouTube.


    6. A must-read for any machine learning practitioner. The authors elegantly blends theoretical underpinnings with easy-to-follow examples. However, as indicated on the book's cover, this is a book on fundamentals. You need to consult other books to see how the principles presented in this book play out in specific techniques. FYI, Dr. Abu-Mostafa has a class based on this book, which is available on Youtube.


    7. This is one of the greatest machine learning books available in the market. Prof Yaser and the co-authers have done a very good job in conveying the fundamentals of the subject so that you can easily catch up the complex topics from there on. The video lecture series available on his site can add value to the reading, and his way of explaining complex topics is second to none.


    8. Besides Andrew Ng's machine learning course on coursera, probably the best guide to machine learning I've used.


    9. Excellent introduction to the theory of Machine Learning, I think they put it well themselves: it is a short course, but not a hurried course. Worth picking up a second time.



    10. The book spends most of the start trying to answer the question "can one learn from the data". It is definitely an interesting question but past that the book doesn't have much more to offer.For such a diverse field, this is definitely not an introduction I would recommend since it fails to give an overview of anything more complex than a linear regression.


    11. A superb little book. Very insightful and well written, and a great value. Definitely start with reading this one!



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