Read Learning From Data: A Short Course by Yaser S. Abu-Mostafa Malik Magdon-Ismail Hsuan-Tien Lin Online

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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....

Title : Learning From Data: A Short Course
Author :
Rating :
ISBN : 9781600490064
Format Type : Hardcover
Number of Pages : 213 Pages
Status : Available For Download
Last checked : 21 Minutes ago!

Learning From Data: A Short Course Reviews

  • Zarathustra Goertzel
    2018-12-09 08:45

    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 linear, supervised ones were actually discussed. This is okay as the focus is on learning itself more than specific methods (and 3-4 more are covered in e-chapters).The excercises throughout prompt the right questions, and the problems lead you into more depth (just reading over them should teach one a lot more :x).Definitely recommended to anyone interested in learning (who can read basic linear maths) :3

  • Romann Weber
    2018-12-10 08:55

    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.

  • Debasish Ghosh
    2018-12-05 09:11

    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.

  • Emil
    2018-11-20 06:07

    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. This book gives a solid base on the theory of ML.

  • Howard B.
    2018-12-01 08:11

    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.

  • Zhaodan Kong
    2018-11-17 10:47

    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.

  • Fazlan
    2018-11-23 10:53

    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.

  • J. Nick
    2018-11-16 13:04

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

  • Jethro Kuan
    2018-11-20 09:51

    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.

  • Camille Birbes
    2018-11-13 10:10

    Challenging, but rewarding. Best used alongside the freely available lectures on Youtube.

  • Azeem Bande-ali
    2018-12-06 13:04

    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.

  • Joshua
    2018-11-17 13:42

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

  • Mark
    2018-11-16 08:57

    ok