Trailer Õ Learning From Data PDF by ↠´ Yaser S. Abu-Mostafa 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.
This is a very good and short introduction on the problem of Learning From Data I also watched the Caltech lectures done by Yaser while I read the book They are some of the best lectures I ve had There is a couple of online chapters as well that effectively doubles the size of the book, but I have only had a good look at the online chapter on SVM s.
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.
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.
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.
there s some pretty hardcore math in this book so I didn t fully understand it all, but it s one of the best machine learning books I ve ever read.
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.
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.
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