quotes Machine Learning: An Applied Mathematics Introduction, litcharts Machine Learning: An Applied Mathematics Introduction, symbolism Machine Learning: An Applied Mathematics Introduction, summary shmoop Machine Learning: An Applied Mathematics Introduction, Machine Learning: An Applied Mathematics Introduction 35e112fd Machine Learning An Applied Mathematics Introduction Covers The Essential Mathematics Behind All Of The Following Topics K Nearest Neighbours K Means Clustering Na Ve Bayes Classifier Regression Methods Support Vector Machines Self Organizing Maps Decision Trees Neural Networks Reinforcement LearningPaul Wilmott Brings Three Decades Of Experience In Education, And His Inimitable Style, To This, The Hottest Of Subjects This Book Is An Accessible Introduction For Anyone Who Wants To Understand The Foundations But Also Wants To Get To The Meat Without Having To Eat Too Many Vegetables Paul Wilmott Has Been Called Cult Derivatives Lecturer By The Financial Times And Financial Mathematics Guru By The BBC

3 thoughts on “Machine Learning: An Applied Mathematics Introduction”

Focused introduction to ML Theory backed by examples which give necessary overview in this field Easy and most important entertaining read in Paul Wilmott style.

Great book on intuition behind broad spectrum of Machine Learning approaches, full of practical examples In fact, it is the only book aside from the Elements of Statistical Learning that I would recommend and own It is in strike contrast to the plethora of ML books on the market that are either too math heavy with little practical examples, or just show you how to apply python or R packages.Finally, entertaining value of this book should not be overlooked, not P G Wodehouse but close.

Focused introduction to ML Theory backed by examples which give necessary overview in this field Easy and most important entertaining read in Paul Wilmott style.

Great book on intuition behind broad spectrum of Machine Learning approaches, full of practical examples In fact, it is the only book aside from the Elements of Statistical Learning that I would recommend and own It is in strike contrast to the plethora of ML books on the market that are either too math heavy with little practical examples, or just show you how to apply python or R packages.Finally, entertaining value of this book should not be overlooked, not P G Wodehouse but close.

I would love to buy an electronic version.