Machine Learning: An Applied Mathematics Introduction

The author gives a very good review of machine learning in theory or from an algorithmic point of view. You don't see a single line of code, but you will be very familiar with the concepts implemented in ML packages like Sci-kit learn. Actually, it'll help to understand what's done in Python. If Sci-kit learn package is a Python library, this book will help "to explain what the code is doing" (page 7). I think the people who knows ML well can learn a lot from this short book - it's relevant and up to date. The writing style is straightforward and fun to read!

Coming from a non mathematical backround the explanation of each algorithm and idea presented in the book was very easy to grasp (although I got lost when trying to follow the more advanced equations). This book has really improved my understanding on the topic and I am giving it a second read to fully understand all the math. This book is a good choice for a layman who wants to dive head first into the topic and doesn't mind having some of the mathematical principles goes over their head the first run through.

Due to not taking the book's title at face value, I didn't realize what I was getting into. I misappropriated the "introduction" term and as such, it left me disappointed with the book. This is not the book's fault however.This is the type of book that's useful if you have a strong foundation in math. There's it's subtitle is "...An Applied Mathematics Introduction."It is such that if you do have strong math skills, then this book will be of great importance to you as you understand how to apply math towards machine learning.If you are like me and learning machine learning on your own and don't quite have the mathematical foundation then it will be a high hurdle to overcome as you read.The book is broken down into chapters which cover various machine learning methodologies. They give a quick synopsis of what the methodology is and when you would use. Then the math begins.It is by no means heavy on math, but it is rich in math. I do enjoy Wilmott's writing style but for me, I need a more basic introduction towards math and due to my own misreading of the title, thought this would aid me.For a pop culture analogy: there's a Simpsons episode where Homer is reading a book on advanced marketing. He doesn't grasp it and in the next scene he is reading a book on beginning marketing. He doesn't grasp that either and then reads the definition of marketing in a dictionary. For me, this book is like the advanced marketing book when I need to learn the definitions first.

When I started out, I ran several trading desks on the financial futures floors at the CME and CBOT. Fundamental and technical analysis were all that existed. I found that the only way to learn the quantitative aspect of the markets (circa 1983) was by walking around the exchange floors right after the close, picking up research/strategy papers off the floor near the most quantitatively-oriented firms. Fortunately for us, books authored by Dr. Wilmott and others like him have shed a light into the math, minds, and methodology of one of the most interesting areas of global markets.

This is short and mostly readable account of some of the main ideas in machine learning, including brief coverage of neural nets. It's well-organized and there are lots of useful examples and diagrams.Take the subtitle of this book ("an applied mathematics introduction") seriously. The author says in the prologue that the book is targeted to a narrow audience of applied mathematicians.The author jumps back and forth between very high-level discussion and hand-wavy technical discussion that assumes background in at least multivariable calculus and linear algebra. Does this work well for applied mathematicians? I think it will disappoint ML beginners without enough math background, and those with some ML experience who are looking for something concise and rigorous.One example is the brief PCA discussion, which assumes you will instantly see that what is needed is to get the eigenvectors of the covariance matrix. Another is the discussion of lasso regression, which suggests that the reader should make a plot and "draw comparisons between minimizing the loss function with penalty term and minimizing the loss function with a constant". (The book Intro to Statistical Learning with R actually explains this connection.)In some parts of the book I like the balance between intuition and technical detail. For example, I like the discussion of bias and variance (but have never found those target pictures very helpful).Some rigorous and concise books on machine learning that I like include Tom Mitchell's class Machine Learning and Hal Daumé III's A Course in Machine Learning.

Great resource. It’s like talking to someone one who is just giving you the simple straight answer to what’s going on. This book’s tone and depth is between the buzz word laden “intro to machine learning” books for business people and the “too much math for non majors” textbooks that focus a specific type of machine learning.With that said I use it to gain an intuition and the first layers of mathematical depth to each ML algorithm. I believe that this does not replace a textbook but more of a straightforward companion. Highly recommend.

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.

Book in good condition, excellent price and contents.