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From Brian Clsrk <>
Subject Re: Recommended reading
Date Tue, 08 Mar 2011 19:03:27 GMT
On 08/03/2011 16:37, Vasil Vasilev wrote:
> Hi all,
> Can someone recommend me good books on Statistics and also on Linear Algebra
> and Analytic Geometry which will provide enough background for understanding
> machine learning algorithms?
> Regards, Vasil
Hi Vasil,

Allow me to present my personal favourites.

As a pre-requisite to probability and statistics, you'll need basic 
calculus.  A maths for scientists text might be useful here such as,

Mathematics for Engineers and Scientists, Alan Jeffrey, Chapman & Hall/CRC.

One of the best writers in the probability/statistics world is Sheldon 
Ross.  Try

A First Course in Probability (8th Edition), Pearson

and then move on to his

Introduction to Probability Models (9th Edition), Academic Press.

Wonderful book.

Some good introductory alternatives here are:

Probability and Statistics (7th Edition), Jay L. Devore, Chapman.

Probability and Statistical Inference (7th Edition), Hogg and Tanis, 

Once you have a grasp of the basics then there are a slew of great texts 
that you might consult:  for example,

Statistical Inference,  Casell and Berger, Duxbury/Thomson Learning.

Most statistics books will have some sort of introduction to Bayesian 
methods, but I recommend a specialist text.  Bolstad writes very clearly 
on Bayesian statistics for the noob: see

Introduction to Bayesian Statistics (2nd Edition), William H. Bolstad, 

Then for the computational side of Bayesian which is predominantly 
Markov chain Monte Carlo you are spoiled for choice!

Try Bolstad's

Understanding Computational Bayesian Statistics, Wiley.

Then you might try the MCMM galacticos

Bayesian Data Analysis, Gelman et al., Chapman &Hall/CRC

On top of the books, R is an indispensable software tool for visualizing 
distributions and doing calculations.

Of course, there is always Wikipedia.

Best of luck,


ps I haven't looked at the recommended literature in 
that Vipul mentioned.  I wonder if I agree with         Stanford?

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