Category Archives: book

C++11 reading list

C++11 (formerly known as C++0x) is the most recent version of the standard of the C++ programming language. After it was approved by ISO in 2011, many books have been published to embrace the updates. Here are THREE core books that I recommend: one language tutorial, one library tutorial, and one bible.

C++ Primer (5th Edition)



Although it is called “primer”, this book is actually written for both beginners and experienced C++ programmers. The 5th Edition is fully updated and recast for C++11 standard as well. As a real tutorial of C++ programming language, it provides authoritative and comprehensive introduction to C++11. Another highlight is its huge amount of examples to help readers learn and understand the language fast.

Recommend: A Course in Machine Learning

The following content is totally copied from the website of A Course in Machine Learning.

CIML is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.). It’s focus is on broad applications with a rigorous backbone. A subset can be used for an undergraduate course; a graduate course could probably cover the entire material and then some.

This book is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it or re-use it under the terms of the CIML License online at ciml.info/LICENSE. You may not redistribute it yourself, but are encouraged to provide a link to the CIML web page for others to download for free. You may not charge a fee for printed versions, though you can print it for your own use.

Book review: Introduction to Machine Learning (2ed)

Introduction to Machine Learning (2ed), by Ethem Alpaydin, MIT Press, 2010. ISBN 0-262-01243-X.

This book provides students, researchers, and developers a comprehensive introduction to the machine learning techniques. It is structured primarily as coursebook, which is a valuable teaching textbook for graduates or undergraduates. This book is also a good resources for self-study by researches and developers, but they have to be familiar with AI and advanced mathematics.

This book begins with an introduction chapter, followed by 18 chapters plus an appendix. Each chapter presents a stand-alone topic, beginning with a brief introduction and ending with notes. Therefore, the readers can quickly obtain an overview for the topic and catch the possible direction to further development in this subject area. The book covers a variety of machine learning techniques: supervised and unsupervised learning, parametric and nonparametric methods. All of these are followed by methods of how to assess and compare classification algorithms, combine multiple learners, and reinforce learning procedure.

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