recap for this week

[Ezekiel Smithburg]

The books amply describe what subjects were covered at this week's meetup.  I strongly encourage everyone to pick a book or three (or ten; there's enough in the list, sheesh) and pursue what they're interested in.  These books are listed because they were mentioned and all seem to be at least decent.  Also, if we standardize a little on our selection of subjects covered/books used, we will likely all make more progress.  I hope, in the next few days, to throw a wiki, blog, and whatever else we need, up on my server for coordinating study, etc.

There seemed to be a consensus on using the following books, for those interested in the subjects:

--variational principles of mechanics

--information theory book

--introduction to scientific programming and simulation using r

--keisler elementary calc

--apostol calc

books mentioned include (but definitely are not limited to):

--calculus of variations

----calculus of variations by gelfand: http://www.amazon.com/Calculus-Variations-I-M-Gelfand/dp/0486414485/ref=sr_1_1?ie=UTF8&s=books&qid=1282020688&sr=8-1
----introduction to the calculus of variations by hans sagan http://www.amazon.com/Introduction-Calculus-Variations-Hans-Sagan/dp/0486673669/ref=sr_1_4?ie=UTF8&s=books&qid=1282020688&sr=8-4
----variational principles of mechanics (we have decided on this book) http://www.amazon.com/Variational-Principles-Mechanics-Physics-Chemistry/dp/0486650677/ref=sr_1_1?ie=UTF8&s=books&qid=1282020713&sr=8-1

--information theory (and two awesome machine learning books because i feel like it)

----information theory http://www.amazon.com/Information-Theory-Inference-Learning-Algorithms/dp/0521642981/ref=sr_1_1?ie=UTF8&s=books&qid=1282020753&sr=8-1

------free, legally, as pdf: http://www.inference.phy.cam.ac.uk/itprnn/

----classic text on statistical learning (another term for machine learning) http://www.amazon.com/Elements-Statistical-Learning-Prediction-Statistics/dp/0387848576/ref=sr_1_6?ie=UTF8&s=books&qid=1282021803&sr=8-6

------this book is also free, legally, as pdf: http://www-stat.stanford.edu/~tibs/ElemStatLearn/

----a slightly more accessible introduction, but seems to be just about as thorough.  more oriented towards practice.  i am reading this one now.  http://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/ref=sr_1_1?ie=UTF8&s=books&qid=1282021803&sr=8-1

--r/statistics

----introduction to scientific programming and simulation using r http://www.amazon.com/gp/product/1420068725/ref=ar_gw_dp

----Introductory Statistics with R http://www.amazon.com/gp/product/0387790535/ref=ar_gw_dp

----ggplot2 elgent graphics for data analysis with r http://www.amazon.com/gp/product/0387981403/ref=ar_gw_dp

----statistics in a nutshell http://www.amazon.com/Statistics-Nutshell-Desktop-Reference-OReilly/dp/0596510497/ref=sr_1_1?ie=UTF8&s=books&qid=1282022675&sr=8-1

----all of statistics http://www.amazon.com/All-Statistics-Concise-Statistical-Inference/dp/1441923225/ref=sr_1_1?ie=UTF8&s=books&qid=1282022661&sr=8-1

--category theory/mathematical physics (the two crossover in some of these links)

----n category cafe http://golem.ph.utexas.edu/category/

----this week in mathematical physics http://math.ucr.edu/home/baez/TWF.html

----the catsters videos http://www.youtube.com/user/TheCatsters (good (but fast paced) youtube videos on category theory)

--interesting calculus books

----calculus by apostol (SPENDY, but great.) http://www.amazon.com/Calculus-Vol-One-Variable-Introduction-Algebra/dp/0471000051/ref=sr_1_1?ie=UTF8&s=books&qid=1282022747&sr=8-1

----elementary calculus, an infitesimal approach (nonstandard analysis based calc!) http://www.math.wisc.edu/~keisler/calc.html (free pdf, out of print.)

[Geodesy]

See http://www.scss.tcd.ie/Edsko.de.Vries/ct/catsters/

[Ezekiel Smithburg]

Awesome find!  I'll have to play with this. =D

On Tue, Aug 17, 2010 at 4:12 PM, Geodesy <geod...@gmail.com> wrote:

See http://www.scss.tcd.ie/Edsko.de.Vries/ct/catsters/

[Geodesy]

Vis-à-vis Haskel, Category Theory, etc.you might find this
interesting:

"Abstract Nonsense for Functional Programmers." (from
http://www.scss.tcd.ie/Edsko.de.Vries/talks/ )
(Slides) Discusses basic category theory (categories, functors, Hom
functors, algebras, monads, natural transformations, and the Yoneda
lemma) from a functional programmer's perspective. (The talk also
briefly discussed the partiality monad, but I removed those two slides
as they contained the erroneous fixpoint definition mentioned above
for Partiality for Dummies.)
http://www.scss.tcd.ie/Edsko.de.Vries/talks/cattheory.pdf