*A First Course in Probability*, S. Ross, 9th ed. (2012)- older editions are also fine.

*Data Analysis: A Bayesian Tutorial*, D. Sivia, J. Skilling (2006)Introductory and practical, teaches everything with examples, but definitely not mathless. Great book.

*Bayesian Data Analysis*, A. Gelman, et al. 2nd ed. (2003)Assumes a healthy amount of statistical background.

*Mathematical Statistics and Data Analysis*, J. Rice, 3rd ed. (2006)- older editions are also probably fine.
- proof-heavy. Familiarity with multivariate calculus and linear algebra is assumed
- includes introductions to all sorts of distributions, and Maximum Likelihood Estimation

*Basic Econometrics*, D. Guajarati and D. Porter, 5th ed. (2008)- with minimal calculus/linear algebra
- relevant chapters to lectures are Ch. 1 - Ch. 13
- older editions are also fine.
Datascope has a copy here. You can borrow this book during the day, or for an evening.

*Linear Models with R*, J. Faraway, 1st ed. (2004)- short, dense book with examples in R
- relevant chapters to lectures are Ch. 1 - Ch. 8, Ch. 9.2-9.3, Ch. 11
familiarity with multivariate calculus and linear algebra is assumed

*Pattern Recognition and Machine Learning*, C.M. Bishop (2007)**The**Machine Learning book. Bayesian viewpoint. Large coverage. Ugly cover.- Heavy enough for use as a murder weapon
We have two copies here. You can borrow this book during the day, or for an evening.

*Machine Learning: A Probabilistic Perspective*, K.P. Murphy (2012)- MIT's take on machine learning. Intuitive, uses lots of examples.
- Self contained, no need for another machine learning book
- You can also murder with this one
We have two copies here. You can borrow this book during the day, or for an evening.

*Elements of Statistical Learning (ESL)*, T. Hastie, R. Tibshirani, J. Friedman, 2nd ed. (2009)- Another Machine Learning classic. Calculus and Linear algebra are assumed.
The pdf is freely available online: http://statweb.stanford.edu/~tibs/ElemStatLearn/

*Categorical Data Analysis*, A. Agresti, 3rd ed. (2012)- Previous ed. (2nd) good too
- On the dense/mathy side, but it is a good reference for GLMs, especially Ch. 1, 4, 5, 6, 8, 9
Agresti also has great, though terse lecture notes from one of his classes

- GLM Notes from a Princeton class
- http://data.princeton.edu/wws509/
Clear lecture notes, relatively digestible for all main topics related to GLM