***This is a list of books I have either used or come across***

***I’ll try to keep the list in some kind of logical order ***

**Bayesian Reasoning and Machine Learning. **Barber, D.

I have only flipped through this book. A PDF version is available at the book’s website.

Level: Graduate

**The Elements of Statistical Learning. ***Hastie, T., Tibshirani, R. and Friedman, J.*

I am currently taking a course in Statistical Learning and the class uses this textbook. It is written in a very technical manner and is nonlinear in organization (there are many references in the text to later chapters), but athors are very thourogh in their coverage of the topic. A free PDF version is available at the book’s website.

Level: Graduate/Advanced Graduate

**A First Course in Probability.** *Ross, S.*

A classic textbook that most first year graduates in statistics will use for class. This is a great book for someone wanting to understand the nuts and bolts of probability. There is a great deal of examples in the book and solutions to most of the problems. An understanding of multivariate calculus is needed to fully appreciate the subject matter.

Level: Advanced Undergraduate/Graduate

**Learning From Data**. *Abu-Mostofa, Y.S., Magdon-Ismail, M. and Lin, H.T*.*
*This book was written toe accompanying Caltech course with the same name. This book is extremely well written and the video lectures that go along with course are as exceptional. This book is also dirt cheap and of high quality printing. If you are in a Machine Learning course and feel like you need some extra insight into the subject, this book is a great aid.

Level: Graduate

**Mining of Massive Datasets**. *Rajaraman, A. and Ullman, J.*

I have only read the first one and a half chapters, but think the book is nicely written and well suited for self study. This fist chapter on data mining acts as good introduction to the subject. The second chapter gives a detailed example of MapReduce using the popular word count example. Even though most people have seen this example more than once, the authors do good job dissecting the algorithm and the options it offers. A PDF version of the article can be download at the book’s website.

Level: Graduate

**New Advances in Machine Learning (Ed.). ***Zhang, Yagang*

This is an open access book that I heard about via @KirkDBorne on Twitter. Below is the publishers description of of the book:

“The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning…. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students… The wide scope of the book provides a good introduction to many approaches of machine learning, and it is also the source of useful bibliographical information”.

The fist three chapters act as a good introduction to machine learning in general and the topics become more specialized as the book progresses. You can download the book through InTech.

**Random Number Generation and Monte Carlo Methods. ***Gentle, J.E.
*I had the pleasure of taking this course from the author a few years ago. This book does a good job of covering the topic. The first two chapters cover the number theory behind random number generation, how it works and its limitations. The book provides pseudocode of the discussed algorithms. Monte Carlo Methods have an interesting history of development.

Level: Graduate/Advanced Graduate