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Posted to user@mahout.apache.org by Vasil Vasilev <va...@gmail.com> on 2011/03/08 17:37:56 UTC
Recommended reading
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
Re: Recommended reading
Posted by Michael Morrison <mw...@gmail.com>.
Hey Jeremy, One that was very helpful to me was "Mathematical Tools for
Applied Mulitvariate Analysis" by J.Douglass Carroll.
http://www.amazon.com/Mathematical-Tools-Applied-Multivariate-Analysis/dp/0121609553/ref=sr_1_1?ie=UTF8&qid=1299602805&sr=8-1
Mike
On Tue, Mar 8, 2011 at 8:42 AM, <je...@lewi.us> wrote:
> Quoting Vasil Vasilev <va...@gmail.com>:
>
> 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
>>
>>
> The goto book for linear algebra is "Introduction to Linear Algebra" by
> Gilbert Strang. His lectures are also available online at.
>
> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
>
> You could probably also find a machine learning class lecture online at
> MIT.
>
> J
>
>
Re: Recommended reading
Posted by Ted Dunning <te...@gmail.com>.
Exactly. Glob and van loan is the standard for computational linear algebra.
Sent from my iPhone
On Mar 8, 2011, at 7:59 PM, Vipul Pandey <vi...@gmail.com> wrote:
> Also, while we are at it : any suggestions on a book on matrix
> computations/decomposition/factorization etc.?
>
> How's this one?
> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
Re: Recommended reading
Posted by Krishna Sankar <ks...@gmail.com>.
For Matrix computations, I like Watkins[1] better.
Cheers
<k/>
[1]
http://www.amazon.com/Fundamentals-Matrix-Computations-Applied-Mathematics/
dp/0470528338/
On 3/8/11 Tue Mar 8, 11, "Vipul Pandey" <vi...@gmail.com> wrote:
>> I also recommend the iTunesU course and the notes to this class. I
>> actually printed out a copy of the lecture notes and had them spiral
>> bound.
>I'm not surprised. I took that class and I believe Andrew Ng does a *very
>good job teaching the course.
>But going through the videos and the lecture notes should be sufficient to
>learn a great deal on one's own.
>
>I also watched Strang's lecture sometime back (again on iTunesU) and they
>are pretty good too - haven't read his book though.
>
>Also, while we are at it : any suggestions on a book on matrix
>computations/decomposition/factorization etc.?
>
>How's this one?
>http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd
>_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101
>&pf_rd_p=470938811&pf_rd_i=507846
>any idea? any other suggestion?
>
>Thanks
>~Vipul
>
>On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com> wrote:
>
>> They won't. Strang is the answer for the linear algebra part.
>>
>> On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
>>
>> > I also recommend the iTunesU course and the notes to this class. I
>> > actually printed out a copy of the lecture notes and had them spiral
>> > bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
>> > sure if those will help as much with the linear algebra though (except
>> > the appendix).
>> >
>> > On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
>> wrote:
>> > > For Machine Learning - i would recommend looking at
>>cs229.stanford.edu
>> .
>> > It's a very nicely taught course with super helpful lecture notes -
>>and
>> you
>> > can get all the videos in youtube or iTunesU.
>> > > http://itunes.apple.com/itunes-u/machine-learning/id384233048
>> > >
>> > > The section notes for this course will give you enough review
>>material
>> on
>> > linear algebra and probability theory to get you going.
>> > >
>> > > ~Vipul
>> > >
>> > >
>> > > On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
>> > >
>> > >> Quoting Vasil Vasilev <va...@gmail.com>:
>> > >>
>> > >>> 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
>> > >>>
>> > >>
>> > >> The goto book for linear algebra is "Introduction to Linear
>>Algebra"
>> by
>> > Gilbert Strang. His lectures are also available online at.
>> > >>
>> > >>
>> >
>>http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
>> > >>
>> > >> You could probably also find a machine learning class lecture
>>online
>> at
>> > MIT.
>> > >>
>> > >> J
>> > >>
>> > >
>> > >
>> >
>> >
>> >
>> > --
>> > Michael Nute
>> > Mike.Nute@gmail.com
>> >
>>
Re: Recommended reading
Posted by Vasil Vasilev <va...@gmail.com>.
Guys,
Thank you a lot for your recommendations. Now I have a really good list to
select from :)
About the online course of Andrew Ng it is a really cool one and it was my
first starting point into the world of machine learning. I would also warmly
recommend it to everyone that is starting up in this area.
On Wed, Mar 9, 2011 at 8:53 AM, Ted Dunning <te...@gmail.com> wrote:
> Golub and van loan talk about parallelization but entirely on the context
> of shared memory. The cost trade offs are very different in map reduce where
> iterations are so expensive
>
> Sent from my iPhone
>
> On Mar 8, 2011, at 8:23 PM, Christopher Schilling <
> chris@thecleversense.com> wrote:
>
> > I second a text on matrix computations, especially if there is text on
> > parallelization of the computation!
> >
> > On Tue, Mar 8, 2011 at 7:59 PM, Vipul Pandey <vi...@gmail.com> wrote:
> >
> >>> I also recommend the iTunesU course and the notes to this class. I
> >>> actually printed out a copy of the lecture notes and had them spiral
> >>> bound.
> >> I'm not surprised. I took that class and I believe Andrew Ng does a
> *very
> >> good job teaching the course.
> >> But going through the videos and the lecture notes should be sufficient
> to
> >> learn a great deal on one's own.
> >>
> >> I also watched Strang's lecture sometime back (again on iTunesU) and
> they
> >> are pretty good too - haven't read his book though.
> >>
> >> Also, while we are at it : any suggestions on a book on matrix
> >> computations/decomposition/factorization etc.?
> >>
> >> How's this one?
> >>
> >>
> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
> >> any idea? any other suggestion?
> >>
> >> Thanks
> >> ~Vipul
> >>
> >> On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com>
> wrote:
> >>
> >>> They won't. Strang is the answer for the linear algebra part.
> >>>
> >>> On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
> >>>
> >>>> I also recommend the iTunesU course and the notes to this class. I
> >>>> actually printed out a copy of the lecture notes and had them spiral
> >>>> bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
> >>>> sure if those will help as much with the linear algebra though (except
> >>>> the appendix).
> >>>>
> >>>> On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
> >>> wrote:
> >>>>> For Machine Learning - i would recommend looking at
> >> cs229.stanford.edu
> >>> .
> >>>> It's a very nicely taught course with super helpful lecture notes -
> and
> >>> you
> >>>> can get all the videos in youtube or iTunesU.
> >>>>> http://itunes.apple.com/itunes-u/machine-learning/id384233048
> >>>>>
> >>>>> The section notes for this course will give you enough review
> >> material
> >>> on
> >>>> linear algebra and probability theory to get you going.
> >>>>>
> >>>>> ~Vipul
> >>>>>
> >>>>>
> >>>>> On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> >>>>>
> >>>>>> Quoting Vasil Vasilev <va...@gmail.com>:
> >>>>>>
> >>>>>>> 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
> >>>>>>>
> >>>>>>
> >>>>>> The goto book for linear algebra is "Introduction to Linear Algebra"
> >>> by
> >>>> Gilbert Strang. His lectures are also available online at.
> >>>>>>
> >>>>>>
> >>>>
> >>
> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
> >>>>>>
> >>>>>> You could probably also find a machine learning class lecture online
> >>> at
> >>>> MIT.
> >>>>>>
> >>>>>> J
> >>>>>>
> >>>>>
> >>>>>
> >>>>
> >>>>
> >>>>
> >>>> --
> >>>> Michael Nute
> >>>> Mike.Nute@gmail.com
> >>>>
> >>>
> >>
>
Re: Recommended reading
Posted by Ted Dunning <te...@gmail.com>.
Gene Golub is generally the one who did lots for LAPACK, but Charles van
Loan is pretty awesome as well.
The philosophy in the book is all good even if a few recent approaches
aren't covered and the detailed tradeoffs have changed.
On Wed, Mar 9, 2011 at 3:13 PM, Dmitriy Lyubimov <dl...@gmail.com> wrote:
> I think one or both of them co-authored many practical algorithms
> found in LAPack today. I think some SVD algorithms in particular. but
> they are all iterative in nature. Even bisect ones.
>
> On Tue, Mar 8, 2011 at 10:53 PM, Ted Dunning <te...@gmail.com>
> wrote:
> > Golub and van loan talk about parallelization but entirely on the context
> of shared memory. The cost trade offs are very different in map reduce where
> iterations are so expensive
> >
> > Sent from my iPhone
> >
> > On Mar 8, 2011, at 8:23 PM, Christopher Schilling <
> chris@thecleversense.com> wrote:
> >
> >> I second a text on matrix computations, especially if there is text on
> >> parallelization of the computation!
> >>
> >> On Tue, Mar 8, 2011 at 7:59 PM, Vipul Pandey <vi...@gmail.com>
> wrote:
> >>
> >>>> I also recommend the iTunesU course and the notes to this class. I
> >>>> actually printed out a copy of the lecture notes and had them spiral
> >>>> bound.
> >>> I'm not surprised. I took that class and I believe Andrew Ng does a
> *very
> >>> good job teaching the course.
> >>> But going through the videos and the lecture notes should be sufficient
> to
> >>> learn a great deal on one's own.
> >>>
> >>> I also watched Strang's lecture sometime back (again on iTunesU) and
> they
> >>> are pretty good too - haven't read his book though.
> >>>
> >>> Also, while we are at it : any suggestions on a book on matrix
> >>> computations/decomposition/factorization etc.?
> >>>
> >>> How's this one?
> >>>
> >>>
> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
> >>> any idea? any other suggestion?
> >>>
> >>> Thanks
> >>> ~Vipul
> >>>
> >>> On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com>
> wrote:
> >>>
> >>>> They won't. Strang is the answer for the linear algebra part.
> >>>>
> >>>> On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com>
> wrote:
> >>>>
> >>>>> I also recommend the iTunesU course and the notes to this class. I
> >>>>> actually printed out a copy of the lecture notes and had them spiral
> >>>>> bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
> >>>>> sure if those will help as much with the linear algebra though
> (except
> >>>>> the appendix).
> >>>>>
> >>>>> On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
> >>>> wrote:
> >>>>>> For Machine Learning - i would recommend looking at
> >>> cs229.stanford.edu
> >>>> .
> >>>>> It's a very nicely taught course with super helpful lecture notes -
> and
> >>>> you
> >>>>> can get all the videos in youtube or iTunesU.
> >>>>>> http://itunes.apple.com/itunes-u/machine-learning/id384233048
> >>>>>>
> >>>>>> The section notes for this course will give you enough review
> >>> material
> >>>> on
> >>>>> linear algebra and probability theory to get you going.
> >>>>>>
> >>>>>> ~Vipul
> >>>>>>
> >>>>>>
> >>>>>> On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> >>>>>>
> >>>>>>> Quoting Vasil Vasilev <va...@gmail.com>:
> >>>>>>>
> >>>>>>>> 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
> >>>>>>>>
> >>>>>>>
> >>>>>>> The goto book for linear algebra is "Introduction to Linear
> Algebra"
> >>>> by
> >>>>> Gilbert Strang. His lectures are also available online at.
> >>>>>>>
> >>>>>>>
> >>>>>
> >>>
> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
> >>>>>>>
> >>>>>>> You could probably also find a machine learning class lecture
> online
> >>>> at
> >>>>> MIT.
> >>>>>>>
> >>>>>>> J
> >>>>>>>
> >>>>>>
> >>>>>>
> >>>>>
> >>>>>
> >>>>>
> >>>>> --
> >>>>> Michael Nute
> >>>>> Mike.Nute@gmail.com
> >>>>>
> >>>>
> >>>
> >
>
Re: Recommended reading
Posted by Dmitriy Lyubimov <dl...@gmail.com>.
I think one or both of them co-authored many practical algorithms
found in LAPack today. I think some SVD algorithms in particular. but
they are all iterative in nature. Even bisect ones.
On Tue, Mar 8, 2011 at 10:53 PM, Ted Dunning <te...@gmail.com> wrote:
> Golub and van loan talk about parallelization but entirely on the context of shared memory. The cost trade offs are very different in map reduce where iterations are so expensive
>
> Sent from my iPhone
>
> On Mar 8, 2011, at 8:23 PM, Christopher Schilling <ch...@thecleversense.com> wrote:
>
>> I second a text on matrix computations, especially if there is text on
>> parallelization of the computation!
>>
>> On Tue, Mar 8, 2011 at 7:59 PM, Vipul Pandey <vi...@gmail.com> wrote:
>>
>>>> I also recommend the iTunesU course and the notes to this class. I
>>>> actually printed out a copy of the lecture notes and had them spiral
>>>> bound.
>>> I'm not surprised. I took that class and I believe Andrew Ng does a *very
>>> good job teaching the course.
>>> But going through the videos and the lecture notes should be sufficient to
>>> learn a great deal on one's own.
>>>
>>> I also watched Strang's lecture sometime back (again on iTunesU) and they
>>> are pretty good too - haven't read his book though.
>>>
>>> Also, while we are at it : any suggestions on a book on matrix
>>> computations/decomposition/factorization etc.?
>>>
>>> How's this one?
>>>
>>> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
>>> any idea? any other suggestion?
>>>
>>> Thanks
>>> ~Vipul
>>>
>>> On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com> wrote:
>>>
>>>> They won't. Strang is the answer for the linear algebra part.
>>>>
>>>> On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
>>>>
>>>>> I also recommend the iTunesU course and the notes to this class. I
>>>>> actually printed out a copy of the lecture notes and had them spiral
>>>>> bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
>>>>> sure if those will help as much with the linear algebra though (except
>>>>> the appendix).
>>>>>
>>>>> On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
>>>> wrote:
>>>>>> For Machine Learning - i would recommend looking at
>>> cs229.stanford.edu
>>>> .
>>>>> It's a very nicely taught course with super helpful lecture notes - and
>>>> you
>>>>> can get all the videos in youtube or iTunesU.
>>>>>> http://itunes.apple.com/itunes-u/machine-learning/id384233048
>>>>>>
>>>>>> The section notes for this course will give you enough review
>>> material
>>>> on
>>>>> linear algebra and probability theory to get you going.
>>>>>>
>>>>>> ~Vipul
>>>>>>
>>>>>>
>>>>>> On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
>>>>>>
>>>>>>> Quoting Vasil Vasilev <va...@gmail.com>:
>>>>>>>
>>>>>>>> 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
>>>>>>>>
>>>>>>>
>>>>>>> The goto book for linear algebra is "Introduction to Linear Algebra"
>>>> by
>>>>> Gilbert Strang. His lectures are also available online at.
>>>>>>>
>>>>>>>
>>>>>
>>> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
>>>>>>>
>>>>>>> You could probably also find a machine learning class lecture online
>>>> at
>>>>> MIT.
>>>>>>>
>>>>>>> J
>>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Michael Nute
>>>>> Mike.Nute@gmail.com
>>>>>
>>>>
>>>
>
Re: Recommended reading
Posted by Ted Dunning <te...@gmail.com>.
Golub and van loan talk about parallelization but entirely on the context of shared memory. The cost trade offs are very different in map reduce where iterations are so expensive
Sent from my iPhone
On Mar 8, 2011, at 8:23 PM, Christopher Schilling <ch...@thecleversense.com> wrote:
> I second a text on matrix computations, especially if there is text on
> parallelization of the computation!
>
> On Tue, Mar 8, 2011 at 7:59 PM, Vipul Pandey <vi...@gmail.com> wrote:
>
>>> I also recommend the iTunesU course and the notes to this class. I
>>> actually printed out a copy of the lecture notes and had them spiral
>>> bound.
>> I'm not surprised. I took that class and I believe Andrew Ng does a *very
>> good job teaching the course.
>> But going through the videos and the lecture notes should be sufficient to
>> learn a great deal on one's own.
>>
>> I also watched Strang's lecture sometime back (again on iTunesU) and they
>> are pretty good too - haven't read his book though.
>>
>> Also, while we are at it : any suggestions on a book on matrix
>> computations/decomposition/factorization etc.?
>>
>> How's this one?
>>
>> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
>> any idea? any other suggestion?
>>
>> Thanks
>> ~Vipul
>>
>> On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com> wrote:
>>
>>> They won't. Strang is the answer for the linear algebra part.
>>>
>>> On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
>>>
>>>> I also recommend the iTunesU course and the notes to this class. I
>>>> actually printed out a copy of the lecture notes and had them spiral
>>>> bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
>>>> sure if those will help as much with the linear algebra though (except
>>>> the appendix).
>>>>
>>>> On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
>>> wrote:
>>>>> For Machine Learning - i would recommend looking at
>> cs229.stanford.edu
>>> .
>>>> It's a very nicely taught course with super helpful lecture notes - and
>>> you
>>>> can get all the videos in youtube or iTunesU.
>>>>> http://itunes.apple.com/itunes-u/machine-learning/id384233048
>>>>>
>>>>> The section notes for this course will give you enough review
>> material
>>> on
>>>> linear algebra and probability theory to get you going.
>>>>>
>>>>> ~Vipul
>>>>>
>>>>>
>>>>> On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
>>>>>
>>>>>> Quoting Vasil Vasilev <va...@gmail.com>:
>>>>>>
>>>>>>> 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
>>>>>>>
>>>>>>
>>>>>> The goto book for linear algebra is "Introduction to Linear Algebra"
>>> by
>>>> Gilbert Strang. His lectures are also available online at.
>>>>>>
>>>>>>
>>>>
>> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
>>>>>>
>>>>>> You could probably also find a machine learning class lecture online
>>> at
>>>> MIT.
>>>>>>
>>>>>> J
>>>>>>
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Michael Nute
>>>> Mike.Nute@gmail.com
>>>>
>>>
>>
Re: Recommended reading
Posted by Ramesh Thulasiram <ys...@gmail.com>.
This conversation is turning out to be very useful. Here is my experience.
I had to learn linear algebra as I had taken a course on optimization
algorithms. I wanted to do it as a crash course, and tried to search for a
book which will make a fast yet good account. I researched about a dozen
books. Gilbert Strang was a great book. But I found the one by Peter V.
O'Neil.. "Introduction to Linear Algebra", to be a great book for beginners
(with some knowledge in calculus). It is not comprehensive, but, I believe,
it will be a good place to start and the author starts by explaining the
concepts with regards to vector spaces which I found to be a more natural
way of explaining.
On Tue, Mar 8, 2011 at 9:23 PM, Christopher Schilling <
chris@thecleversense.com> wrote:
> I second a text on matrix computations, especially if there is text on
> parallelization of the computation!
>
> On Tue, Mar 8, 2011 at 7:59 PM, Vipul Pandey <vi...@gmail.com> wrote:
>
> > > I also recommend the iTunesU course and the notes to this class. I
> > > actually printed out a copy of the lecture notes and had them spiral
> > > bound.
> > I'm not surprised. I took that class and I believe Andrew Ng does a *very
> > good job teaching the course.
> > But going through the videos and the lecture notes should be sufficient
> to
> > learn a great deal on one's own.
> >
> > I also watched Strang's lecture sometime back (again on iTunesU) and
> they
> > are pretty good too - haven't read his book though.
> >
> > Also, while we are at it : any suggestions on a book on matrix
> > computations/decomposition/factorization etc.?
> >
> > How's this one?
> >
> >
> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
> > any idea? any other suggestion?
> >
> > Thanks
> > ~Vipul
> >
> > On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com>
> wrote:
> >
> > > They won't. Strang is the answer for the linear algebra part.
> > >
> > > On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
> > >
> > > > I also recommend the iTunesU course and the notes to this class. I
> > > > actually printed out a copy of the lecture notes and had them spiral
> > > > bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
> > > > sure if those will help as much with the linear algebra though
> (except
> > > > the appendix).
> > > >
> > > > On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
> > > wrote:
> > > > > For Machine Learning - i would recommend looking at
> > cs229.stanford.edu
> > > .
> > > > It's a very nicely taught course with super helpful lecture notes -
> and
> > > you
> > > > can get all the videos in youtube or iTunesU.
> > > > > http://itunes.apple.com/itunes-u/machine-learning/id384233048
> > > > >
> > > > > The section notes for this course will give you enough review
> > material
> > > on
> > > > linear algebra and probability theory to get you going.
> > > > >
> > > > > ~Vipul
> > > > >
> > > > >
> > > > > On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> > > > >
> > > > >> Quoting Vasil Vasilev <va...@gmail.com>:
> > > > >>
> > > > >>> 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
> > > > >>>
> > > > >>
> > > > >> The goto book for linear algebra is "Introduction to Linear
> Algebra"
> > > by
> > > > Gilbert Strang. His lectures are also available online at.
> > > > >>
> > > > >>
> > > >
> > http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
> > > > >>
> > > > >> You could probably also find a machine learning class lecture
> online
> > > at
> > > > MIT.
> > > > >>
> > > > >> J
> > > > >>
> > > > >
> > > > >
> > > >
> > > >
> > > >
> > > > --
> > > > Michael Nute
> > > > Mike.Nute@gmail.com
> > > >
> > >
> >
>
Re: Recommended reading
Posted by Christopher Schilling <ch...@thecleversense.com>.
I second a text on matrix computations, especially if there is text on
parallelization of the computation!
On Tue, Mar 8, 2011 at 7:59 PM, Vipul Pandey <vi...@gmail.com> wrote:
> > I also recommend the iTunesU course and the notes to this class. I
> > actually printed out a copy of the lecture notes and had them spiral
> > bound.
> I'm not surprised. I took that class and I believe Andrew Ng does a *very
> good job teaching the course.
> But going through the videos and the lecture notes should be sufficient to
> learn a great deal on one's own.
>
> I also watched Strang's lecture sometime back (again on iTunesU) and they
> are pretty good too - haven't read his book though.
>
> Also, while we are at it : any suggestions on a book on matrix
> computations/decomposition/factorization etc.?
>
> How's this one?
>
> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
> any idea? any other suggestion?
>
> Thanks
> ~Vipul
>
> On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com> wrote:
>
> > They won't. Strang is the answer for the linear algebra part.
> >
> > On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
> >
> > > I also recommend the iTunesU course and the notes to this class. I
> > > actually printed out a copy of the lecture notes and had them spiral
> > > bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
> > > sure if those will help as much with the linear algebra though (except
> > > the appendix).
> > >
> > > On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
> > wrote:
> > > > For Machine Learning - i would recommend looking at
> cs229.stanford.edu
> > .
> > > It's a very nicely taught course with super helpful lecture notes - and
> > you
> > > can get all the videos in youtube or iTunesU.
> > > > http://itunes.apple.com/itunes-u/machine-learning/id384233048
> > > >
> > > > The section notes for this course will give you enough review
> material
> > on
> > > linear algebra and probability theory to get you going.
> > > >
> > > > ~Vipul
> > > >
> > > >
> > > > On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> > > >
> > > >> Quoting Vasil Vasilev <va...@gmail.com>:
> > > >>
> > > >>> 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
> > > >>>
> > > >>
> > > >> The goto book for linear algebra is "Introduction to Linear Algebra"
> > by
> > > Gilbert Strang. His lectures are also available online at.
> > > >>
> > > >>
> > >
> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
> > > >>
> > > >> You could probably also find a machine learning class lecture online
> > at
> > > MIT.
> > > >>
> > > >> J
> > > >>
> > > >
> > > >
> > >
> > >
> > >
> > > --
> > > Michael Nute
> > > Mike.Nute@gmail.com
> > >
> >
>
Re: Recommended reading
Posted by James Warren <ja...@stanfordalumni.org>.
The Gollub / Van Loan text you mention is the classic text for numerical
linear algebra. Can't go wrong with it. However, I'd also suggest you look
at Nick Trefethen's "Numerical Linear Algebra". It's a bit more
approachable for practitioners -- GVL is better suited for researchers.
cheers,
-James
On Tue, Mar 8, 2011 at 7:59 PM, Vipul Pandey <vi...@gmail.com> wrote:
> > I also recommend the iTunesU course and the notes to this class. I
> > actually printed out a copy of the lecture notes and had them spiral
> > bound.
> I'm not surprised. I took that class and I believe Andrew Ng does a *very
> good job teaching the course.
> But going through the videos and the lecture notes should be sufficient to
> learn a great deal on one's own.
>
> I also watched Strang's lecture sometime back (again on iTunesU) and they
> are pretty good too - haven't read his book though.
>
> Also, while we are at it : any suggestions on a book on matrix
> computations/decomposition/factorization etc.?
>
> How's this one?
>
> http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
> any idea? any other suggestion?
>
> Thanks
> ~Vipul
>
> On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com> wrote:
>
> > They won't. Strang is the answer for the linear algebra part.
> >
> > On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
> >
> > > I also recommend the iTunesU course and the notes to this class. I
> > > actually printed out a copy of the lecture notes and had them spiral
> > > bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
> > > sure if those will help as much with the linear algebra though (except
> > > the appendix).
> > >
> > > On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
> > wrote:
> > > > For Machine Learning - i would recommend looking at
> cs229.stanford.edu
> > .
> > > It's a very nicely taught course with super helpful lecture notes - and
> > you
> > > can get all the videos in youtube or iTunesU.
> > > > http://itunes.apple.com/itunes-u/machine-learning/id384233048
> > > >
> > > > The section notes for this course will give you enough review
> material
> > on
> > > linear algebra and probability theory to get you going.
> > > >
> > > > ~Vipul
> > > >
> > > >
> > > > On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> > > >
> > > >> Quoting Vasil Vasilev <va...@gmail.com>:
> > > >>
> > > >>> 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
> > > >>>
> > > >>
> > > >> The goto book for linear algebra is "Introduction to Linear Algebra"
> > by
> > > Gilbert Strang. His lectures are also available online at.
> > > >>
> > > >>
> > >
> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
> > > >>
> > > >> You could probably also find a machine learning class lecture online
> > at
> > > MIT.
> > > >>
> > > >> J
> > > >>
> > > >
> > > >
> > >
> > >
> > >
> > > --
> > > Michael Nute
> > > Mike.Nute@gmail.com
> > >
> >
>
Re: Recommended reading
Posted by Vipul Pandey <vi...@gmail.com>.
> I also recommend the iTunesU course and the notes to this class. I
> actually printed out a copy of the lecture notes and had them spiral
> bound.
I'm not surprised. I took that class and I believe Andrew Ng does a *very
good job teaching the course.
But going through the videos and the lecture notes should be sufficient to
learn a great deal on one's own.
I also watched Strang's lecture sometime back (again on iTunesU) and they
are pretty good too - haven't read his book though.
Also, while we are at it : any suggestions on a book on matrix
computations/decomposition/factorization etc.?
How's this one?
http://www.amazon.com/gp/product/0801854148/ref=s9_simh_gw_p14_d0_i1?pf_rd_m=ATVPDKIKX0DER&pf_rd_s=center-3&pf_rd_r=0ESQ3KDY8MJ1AWWG8PFR&pf_rd_t=101&pf_rd_p=470938811&pf_rd_i=507846
any idea? any other suggestion?
Thanks
~Vipul
On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com> wrote:
> They won't. Strang is the answer for the linear algebra part.
>
> On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
>
> > I also recommend the iTunesU course and the notes to this class. I
> > actually printed out a copy of the lecture notes and had them spiral
> > bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
> > sure if those will help as much with the linear algebra though (except
> > the appendix).
> >
> > On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com>
> wrote:
> > > For Machine Learning - i would recommend looking at cs229.stanford.edu
> .
> > It's a very nicely taught course with super helpful lecture notes - and
> you
> > can get all the videos in youtube or iTunesU.
> > > http://itunes.apple.com/itunes-u/machine-learning/id384233048
> > >
> > > The section notes for this course will give you enough review material
> on
> > linear algebra and probability theory to get you going.
> > >
> > > ~Vipul
> > >
> > >
> > > On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> > >
> > >> Quoting Vasil Vasilev <va...@gmail.com>:
> > >>
> > >>> 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
> > >>>
> > >>
> > >> The goto book for linear algebra is "Introduction to Linear Algebra"
> by
> > Gilbert Strang. His lectures are also available online at.
> > >>
> > >>
> > http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
> > >>
> > >> You could probably also find a machine learning class lecture online
> at
> > MIT.
> > >>
> > >> J
> > >>
> > >
> > >
> >
> >
> >
> > --
> > Michael Nute
> > Mike.Nute@gmail.com
> >
>
Re: Recommended reading
Posted by Lance Norskog <go...@gmail.com>.
At the Hacker Dojo they taught a Machine Learning class based on the
Stanford CS229 materials. Most of the tutorials were dense but very
well-written.
But the class was quite advanced for most of the programmer
participants. It was 30 years since I had watched someone write Greek
letters in front of a class. A struggle to be sure, but the instructor
loved doing it.
They are now doing two classes, beginning and advanced. The Stanford
CS229 is the advanced. The beginning class uses a stats & other things
course from San Jose State.
The Stanford professor/grad student crew won an autonomous vehicle
competition 2 years in a row. They have this great video of the car
doing a stunt: accelerate in reverse, slam the wheel and brakes, and
do a skidding U-turn backwards into a parking space. They recorded a
stunt driver and set up the car to switch between closed- and
open-loop controls.
On Tue, Mar 8, 2011 at 4:38 PM, Ted Dunning <te...@gmail.com> wrote:
> They won't. Strang is the answer for the linear algebra part.
>
> On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
>
>> I also recommend the iTunesU course and the notes to this class. I
>> actually printed out a copy of the lecture notes and had them spiral
>> bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
>> sure if those will help as much with the linear algebra though (except
>> the appendix).
>>
>> On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com> wrote:
>> > For Machine Learning - i would recommend looking at cs229.stanford.edu.
>> It's a very nicely taught course with super helpful lecture notes - and you
>> can get all the videos in youtube or iTunesU.
>> > http://itunes.apple.com/itunes-u/machine-learning/id384233048
>> >
>> > The section notes for this course will give you enough review material on
>> linear algebra and probability theory to get you going.
>> >
>> > ~Vipul
>> >
>> >
>> > On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
>> >
>> >> Quoting Vasil Vasilev <va...@gmail.com>:
>> >>
>> >>> 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
>> >>>
>> >>
>> >> The goto book for linear algebra is "Introduction to Linear Algebra" by
>> Gilbert Strang. His lectures are also available online at.
>> >>
>> >>
>> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
>> >>
>> >> You could probably also find a machine learning class lecture online at
>> MIT.
>> >>
>> >> J
>> >>
>> >
>> >
>>
>>
>>
>> --
>> Michael Nute
>> Mike.Nute@gmail.com
>>
>
--
Lance Norskog
goksron@gmail.com
Re: Recommended reading
Posted by Ted Dunning <te...@gmail.com>.
They won't. Strang is the answer for the linear algebra part.
On Tue, Mar 8, 2011 at 4:18 PM, Mike Nute <mi...@gmail.com> wrote:
> I also recommend the iTunesU course and the notes to this class. I
> actually printed out a copy of the lecture notes and had them spiral
> bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
> sure if those will help as much with the linear algebra though (except
> the appendix).
>
> On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com> wrote:
> > For Machine Learning - i would recommend looking at cs229.stanford.edu.
> It's a very nicely taught course with super helpful lecture notes - and you
> can get all the videos in youtube or iTunesU.
> > http://itunes.apple.com/itunes-u/machine-learning/id384233048
> >
> > The section notes for this course will give you enough review material on
> linear algebra and probability theory to get you going.
> >
> > ~Vipul
> >
> >
> > On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> >
> >> Quoting Vasil Vasilev <va...@gmail.com>:
> >>
> >>> 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
> >>>
> >>
> >> The goto book for linear algebra is "Introduction to Linear Algebra" by
> Gilbert Strang. His lectures are also available online at.
> >>
> >>
> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
> >>
> >> You could probably also find a machine learning class lecture online at
> MIT.
> >>
> >> J
> >>
> >
> >
>
>
>
> --
> Michael Nute
> Mike.Nute@gmail.com
>
Re: Recommended reading
Posted by Mike Nute <mi...@gmail.com>.
I also recommend the iTunesU course and the notes to this class. I
actually printed out a copy of the lecture notes and had them spiral
bound. The Hastie/Tibshirani/Freidman book is also excellent. Not
sure if those will help as much with the linear algebra though (except
the appendix).
On Tue, Mar 8, 2011 at 12:35 PM, Vipul Pandey <vi...@gmail.com> wrote:
> For Machine Learning - i would recommend looking at cs229.stanford.edu. It's a very nicely taught course with super helpful lecture notes - and you can get all the videos in youtube or iTunesU.
> http://itunes.apple.com/itunes-u/machine-learning/id384233048
>
> The section notes for this course will give you enough review material on linear algebra and probability theory to get you going.
>
> ~Vipul
>
>
> On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
>
>> Quoting Vasil Vasilev <va...@gmail.com>:
>>
>>> 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
>>>
>>
>> The goto book for linear algebra is "Introduction to Linear Algebra" by Gilbert Strang. His lectures are also available online at.
>>
>> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
>>
>> You could probably also find a machine learning class lecture online at MIT.
>>
>> J
>>
>
>
--
Michael Nute
Mike.Nute@gmail.com
Re: Recommended reading
Posted by Vipul Pandey <vi...@gmail.com>.
For Machine Learning - i would recommend looking at cs229.stanford.edu. It's a very nicely taught course with super helpful lecture notes - and you can get all the videos in youtube or iTunesU.
http://itunes.apple.com/itunes-u/machine-learning/id384233048
The section notes for this course will give you enough review material on linear algebra and probability theory to get you going.
~Vipul
On Mar 8, 2011, at 8:42 AM, jeremy@lewi.us wrote:
> Quoting Vasil Vasilev <va...@gmail.com>:
>
>> 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
>>
>
> The goto book for linear algebra is "Introduction to Linear Algebra" by Gilbert Strang. His lectures are also available online at.
>
> http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
>
> You could probably also find a machine learning class lecture online at MIT.
>
> J
>
Re: Recommended reading
Posted by je...@lewi.us.
Quoting Vasil Vasilev <va...@gmail.com>:
> 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
>
The goto book for linear algebra is "Introduction to Linear Algebra"
by Gilbert Strang. His lectures are also available online at.
http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
You could probably also find a machine learning class lecture online at MIT.
J
Re: Recommended reading
Posted by Ted Dunning <te...@gmail.com>.
I have found that learning about R is a difficult thing. The best
introduction I have seen is, paradoxically, not really a book about R and
assumes a statistical mind-set that I disagree with. That introduction is
in MASS (http://www.stats.ox.ac.uk/pub/MASS4/). Other references also
exist:
http://www.r-tutor.com/r-introduction
http://cran.r-project.org/doc/manuals/R-intro.pdf
http://faculty.washington.edu/tlumley/Rcourse/
In addition, you should see how to plot data well:
http://www.statmethods.net/advgraphs/trellis.html
http://had.co.nz/ggplot2/
Generally, I learn more about R by watching people and reading code than by
reading books. There are many small tricks like how to format data
optimally, how to restructure data.frames, common ways to plot data, which
libraries do what and so on that an introductory book cannot convey general
principles that will see you through to success.
On Sun, Mar 27, 2011 at 9:43 PM, Dmitriy Lyubimov <dl...@gmail.com> wrote:
> BTW what about R? There is literally tons of books in R series devoted
> to rather isolated problems but what would be a good crush course
> book?
>
> On Tue, Mar 8, 2011 at 12:11 PM, Ted Dunning <te...@gmail.com>
> wrote:
> > Strang is very hard to beat for linear algebra.
> >
>
Re: Recommended reading
Posted by Dmitriy Lyubimov <dl...@gmail.com>.
BTW what about R? There is literally tons of books in R series devoted
to rather isolated problems but what would be a good crush course
book?
On Tue, Mar 8, 2011 at 12:11 PM, Ted Dunning <te...@gmail.com> wrote:
> Strang is very hard to beat for linear algebra.
>
Re: Recommended reading
Posted by Peter Harrington <pe...@gmail.com>.
I think this is the most relevant book for matrix math on distributed systems:
http://www.amazon.com/Numerical-Linear-Algebra-Lloyd-Trefethen/dp/0898713617
Many chapters on SVD, there are even chapters on Lanczos
On Wed, Mar 9, 2011 at 3:45 PM, Dmitriy Lyubimov <dl...@gmail.com> wrote:
> I have the Bishop book.
>
> It requires a lot of math fundamentals (esp. in statistics which i
> find myself in particular still struggling with a lot) which is fine
> but i think as many commenters noted in Amazon reviews it gives a
> mathematical description of a method but little in terms of practical
> exercises and solutions, even as simple as 'matlab implementation is
> practical up until problem size X'.
>
> Without rebuffing the prerequisite for a good math fluency at all, i
> think there are texts that are more appealing for such a handicap user
> as myself. (I have a Marsland's book that i think doesn't cover as
> much but contains more accessible discussions, and the aforementioned
> Standford class is of course also great). I liked Tricia Hoffman's et.
> al. slides from their Machine Learning classes a lot too for the same
> reason.
>
> On Tue, Mar 8, 2011 at 12:11 PM, Ted Dunning <te...@gmail.com> wrote:
>> 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/en-us/um/people/cmbishop/PRML/index.htm
>>
>> Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome
>> Friedman
>> <http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm>
>> http://www-stat.stanford.edu/~tibs/ElemStatLearn/
>>
>>
>> On Tue, Mar 8, 2011 at 11:03 AM, Brian Clsrk <br...@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 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,
>>> 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?
>>>
>>>
>>>
>>>
>>
>
Re: Recommended reading
Posted by Dmitriy Lyubimov <dl...@gmail.com>.
I have the Bishop book.
It requires a lot of math fundamentals (esp. in statistics which i
find myself in particular still struggling with a lot) which is fine
but i think as many commenters noted in Amazon reviews it gives a
mathematical description of a method but little in terms of practical
exercises and solutions, even as simple as 'matlab implementation is
practical up until problem size X'.
Without rebuffing the prerequisite for a good math fluency at all, i
think there are texts that are more appealing for such a handicap user
as myself. (I have a Marsland's book that i think doesn't cover as
much but contains more accessible discussions, and the aforementioned
Standford class is of course also great). I liked Tricia Hoffman's et.
al. slides from their Machine Learning classes a lot too for the same
reason.
On Tue, Mar 8, 2011 at 12:11 PM, Ted Dunning <te...@gmail.com> wrote:
> 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/en-us/um/people/cmbishop/PRML/index.htm
>
> Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome
> Friedman
> <http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm>
> http://www-stat.stanford.edu/~tibs/ElemStatLearn/
>
>
> On Tue, Mar 8, 2011 at 11:03 AM, Brian Clsrk <br...@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 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,
>> 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?
>>
>>
>>
>>
>
Re: Recommended reading
Posted by Ted Dunning <te...@gmail.com>.
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/en-us/um/people/cmbishop/PRML/index.htm
Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, Jerome
Friedman
<http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm>
http://www-stat.stanford.edu/~tibs/ElemStatLearn/
On Tue, Mar 8, 2011 at 11:03 AM, Brian Clsrk <br...@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 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,
> 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?
>
>
>
>
Re: Recommended reading
Posted by Brian Clsrk <br...@btinternet.com>.
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,
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?
Re: Recommended reading
Posted by Grant Ingersoll <gs...@apache.org>.
FYI: You might also find https://cwiki.apache.org/confluence/display/MAHOUT/Books+Tutorials+and+Talks useful. I've linked the reference reading page from this books page.
On Mar 31, 2011, at 9:27 AM, Isabel Drost wrote:
> On Mon, 28 Mar 11 Dan Brickley wrote:
>> I've collected up much of the text from this mail thread and added it
>> to the wiki at
>> https://cwiki.apache.org/confluence/display/MAHOUT/Reference+Reading
>>
>> I've added links where I could find them, wikified the voice a little
>> (downplayed opinions and some detail), but otherwise the text is from
>> this thread.
>>
>> The page currently is a bit awkward since I appended a large body of
>> text to a pre-existing small entry, but it seemed better than adding
>> a new page. But I'd rather circulate it as-is now than leave this on
>> a 'someday pile', so ... there you go. Hope it's useful and that
>> others are in the mood to jump in and polish / improve the page.
>
> Thanks so much for going to the effort of adding this information to
> the wiki page - sure it needs some polish, however it's nice to see
> some of the wisdom commonly found on the mailing list transferred over
> to the wiki.
>
> Isabel
--------------------------
Grant Ingersoll
Lucene Revolution -- Lucene and Solr User Conference
May 25-26 in San Francisco
www.lucenerevolution.org
Re: Recommended reading
Posted by Isabel Drost <is...@apache.org>.
On Mon, 28 Mar 11 Dan Brickley wrote:
> I've collected up much of the text from this mail thread and added it
> to the wiki at
> https://cwiki.apache.org/confluence/display/MAHOUT/Reference+Reading
>
> I've added links where I could find them, wikified the voice a little
> (downplayed opinions and some detail), but otherwise the text is from
> this thread.
>
> The page currently is a bit awkward since I appended a large body of
> text to a pre-existing small entry, but it seemed better than adding
> a new page. But I'd rather circulate it as-is now than leave this on
> a 'someday pile', so ... there you go. Hope it's useful and that
> others are in the mood to jump in and polish / improve the page.
Thanks so much for going to the effort of adding this information to
the wiki page - sure it needs some polish, however it's nice to see
some of the wisdom commonly found on the mailing list transferred over
to the wiki.
Isabel
Re: Recommended reading
Posted by Dan Brickley <da...@danbri.org>.
On 8 March 2011 17:37, Vasil Vasilev <va...@gmail.com> 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?
>
Hi all
I've collected up much of the text from this mail thread and added it to the
wiki at https://cwiki.apache.org/confluence/display/MAHOUT/Reference+Reading
I've added links where I could find them, wikified the voice a little
(downplayed opinions and some detail), but otherwise the text is from this
thread.
The page currently is a bit awkward since I appended a large body of text to
a pre-existing small entry, but it seemed better than adding a new page. But
I'd rather circulate it as-is now than leave this on a 'someday pile', so
... there you go. Hope it's useful and that others are in the mood to jump
in and polish / improve the page.
cheers,
Dan