- «An Introduction to Statistical Learning: with Applications in R» (2013)
This book is very well appreciated on the Amazon site. It was written by three professors from USC, Stanford and the University of Washington. Three authors: Gareth James, Daniela Witten and Trevor Hastie - all have experience in the field of statistics. The book is more practical than the analogue «The Elements of Statistical Learning» with views of examples of R.
- «The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition» (2011)
Well appreciated the book on Amazon. It was written by three professors of statistics at Stanford. The book seems to be a bit too heavy to read, so some readers might miss it.
- «Pattern Recognition and Machine Learning» (2007)
Highly rated book on Amazon, written by renowned author Christopher M. Bishop, who is a distinguished scientist at Microsoft Research in Cambridge, where it is machine learning. The book is technically understandable. The book covers topics such as regression, linear classification, neural networks, kernel methods, and graphical models.
- «Machine Learning: A Probabilistic Perspective» (2012)
This book provides a set of methods that can automatically detect patterns in the data, and then use the uncoated model to predict future data. The textbook provides a comprehensive introduction to the field of machine learning, based on a single, probabilistic approach. The author, Kevin Murphy, a research scientist at Google, where he is working on the AI, machine learning, computer vision, knowledge base construction and natural language processing.