Strang is very hard to beat for linear algebra.
For statistics related to machine learning, I would avoid normal statistical
texts and go with these instead
Pattern Recognition and Machine Learning by Chris Bishop
http://research.microsoft.com/enus/um/people/cmbishop/PRML/index.htm
Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome
Friedman
<http://research.microsoft.com/enus/um/people/cmbishop/PRML/index.htm>
http://wwwstat.stanford.edu/~tibs/ElemStatLearn/
On Tue, Mar 8, 2011 at 11:03 AM, Brian Clsrk <brian.clark5@btinternet.com>wrote:
> 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.eduthat Vipul mentioned.
I wonder if I agree with Stanford?
>
>
>
>
