Free Data Science eBooks – February 2017

Every month we scour the internet seeking out free eBooks to help you on your educational journey, and this month has been no different.

I hope these books prove to be a valuable resource to you and that you will visit regularly (and invite your friends too).

This month, we have a book about Text Mining, one about Statistics and one about Artificial Intelligence. They're all FREE, so help yourself...

Enjoy!

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data in the belief that these data contain useful knowledge.

Text mining techniques have been studied aggressively in order to extract the knowledge from the data since the late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. There are various techniques from relation extraction to under or less resourced language.

This book will give new knowledge in the text mining field and help many readers open new research fields.

The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly. "Statistical thinking" means a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus. "Likelihood emphasis" means that the likelihood function and likelihood principle are unifying ideas throughout the text.

Another unusual aspect is the use of statistical software as a pedagogical tool. That is, instead of viewing the computer merely as a convenient and accurate calculating device, the book uses computer calculation and simulation as another way of explaining and helping readers understand the underlying concepts. The book is written with the statistical language R embedded throughout.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment.

In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.

The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.