Texts on Various Topics in Statistics (GLMs, MCMC, Decision Trees, etc.) I am currently looking for texts (or preferably a specific text) which have a good balance between theory and application and are as comprehensive as possible and are at an introductory level, covering these topics:

Markov Chains, MCMC, GLMs and extended linear models, Decision Trees, Spatial Statistics, Visualization & Fit, Neural Nets, Ensemble Methods

From my research, it seems that these are topics covered in a master's level statistics program, but I've only had an undergraduate background and don't know what are the standard texts for such topics. 
Edit: Needless to say, I would like more detailed answers/feedback on this topic.
Edit2: The answers below are nice, but I would like answers which address all topics if possible. As mentioned in the bounty description, this list of topics will be covered in a future actuarial exam. No source material has been announced for this exam yet.
 A: Given your interests, for GLMs you might consider De Jong and Heller$^{[1]}$ (if you haven't already read it), and maybe add in the document by Clarke and Thayer (if you haven't already read that). 
[1]: De Jong P. and Heller, G.Z. (2008),
Generalized linear modelling for insurance data,
Cambridge University Press
[2]: Clark, D.R. and Thayer, C.A. (2004),
"A Primer on the Exponential Family of Distributions,"
CAS Discussion Paper Program, Casualty Actuarial Society, p117-148
http://www.casact.org/pubs/dpp/dpp04/04dpp117.pdf
A: I strongly suggest you All of statistics for a basic background. Such as covariance, mean, Test Hypothesis and so on. 
More related to machine learning the best book is absolutely The element of statistical learning 
If you want to go more in the software engineering implementation of this method for big data problem than a good book could be 
Mining massive dataset
I think that would be very difficult - impossible to learn well only from these books in your own. This is because the field is too wide.   
I suggest you to do some free online courses.
The website Udacity as some good course on statistics and machine learning and there is also a very popular course on Coursera.
A: Here is a good list to learn the art of probability & statistics. Here is another set to learn monte carlo methods. Note, you are better off getting a good grounding in statistics and probability before moving onto MCMC methods as they can seem fairly advanced at first look. 
A: take a look at this list. It has interesting links:
http://datascience.sg/resources/
