Histograms. Kernel smoothing. Bayesian statistics.
The lecture can be found here: Lecture7.pdf
Short addition to the lecture: Lecture7_add.pdf
Christopher M. Bishop, "Pattern recognition and machine learning", Springer 2006, ISBN-13: 978-0387-31073-2.
This book is focused on the methods used in data mining and is not strongly directed to particular applications.
Practical quide concentrated on astronomy-specific topics
David J. C. MacKay, "Information Theory, Inference and Learning Algorithms, Cambridge 2003, ISBN: 0-512-64298-1
This is a bit orthogonal to normal data mining books. It can be useful as background but is not central to the course. It is however well written and is also available on the web.
John A. Rice, "Mathematical Statistics and Data Analysis", Duxbury Press 1995, ISBN: 0-534-20934-3
There are many introductory statistics books, this happens to be one I keep around for reference. Any decent statistics book will be sufficient as reference for this course.
RIchard O. Duda, Peter E. Hart & David G. Stork, "Pattern Classification", John WIley & Sons 2001, ISBN-13: 978-0471056690
A classic in the area of data mining/machine learning/pattern recognition. This book has both proponents and detractors, it is a good reference, personally I prefer Bishop but also use this on occasion.
Janine Illian, et al, "Statistical Analysis and Modelling of Spatial Point Patterns", John Wiley & Sons 2008, ISBN-13: 978-0-470-01491-2
The course will not discuss point patterns in great detail (correlation functions probably being the only example), but for the interested student this book discusses this interesting topic in great detail.
From beginners to intermediate level in statistics
PhD student level
Very useful review on the subject