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 prerequisite 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,
Pearson.
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,
Wiley.
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,
Brian
ps I haven't looked at the recommended literature in cs229.stanford.edu
that Vipul mentioned. I wonder if I agree with Stanford?
