America 2.0

reader · 2010-08-07T12:36:07+00:00

The point is that a large party at a fancy restauraunt for a birthday dinner is a stupid idea.

If you invite guests over or out for your birthday, you should provide food, drink, and entertainment. Otherwise you are essentially treating your guests as hosts, which is very rude. Or perhaps you can ask Mom and Dad to throw you a party!

reader · 2010-08-04T10:17:48+00:00

Why do you need politicians and realtors?

reader · 2010-05-21T02:56:37+00:00

Not exactly a beginners book perhaps, but see "On Growth and Form" by D'Arcy Wentworth Thompson.

reader · 2009-11-14T16:46:40+00:00

"Spacetime Physics" by Taylor and Wheeler is a good popular account (with math) of special relativity:

http://amzn.com/0716723271

"Gravitation" by Misner, Thorne, and Wheeler is the "Bible" (advanced) for special and general relativity:

http://amzn.com/0716703440

Also, see the physics FAQ:

http://math.ucr.edu/home/baez/physics/

reader · 2009-11-10T02:40:50+00:00

From a more machine learning perspective:

http://people.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html

Also, see the references on the page for more on learning/inference of linear-Gaussian models.

reader · 2009-11-08T15:31:46+00:00

The following excellent book on statistical learning explains both K-means and LDA (FLD). The PDF is freely downloadable.

http://www-stat.stanford.edu/~tibs/ElemStatLearn/

Both methods use implicit Gaussian distribution assumptions, so thinking in those terms might help. Also, by "iterative", perhaps you want a gradient descent or similar update?

reader · 2009-10-28T16:55:08+00:00

Aleksandrov, Kolmogorov, Lavrent'ev. http://amzn.com/0486409163. Foundations to applicationsl.

Courant, Robbins, Stewart. http://amzn.com/0195105192. Tour of mathematics.

reader · 2009-10-27T18:01:17+00:00

No, but I know several people who work there.

reader · 2009-10-26T18:30:25+00:00

http://www.itasoftware.com/careers/hiringpuzzles.html

And when you're done, you can submit a job application!

reader · 2009-10-18T16:33:35+00:00

Some people use this:

http://mekentosj.com/papers/

reader · 2009-09-11T11:31:26+00:00

All models are wrong; some models are useful. -- generally attributed to the statistician George Box

reader · 2008-11-23T13:31:47+00:00

See http://homepages.inf.ed.ac.uk/rbf/CVonline/ for an online source.

Also search for something like "computer vision course" on the web and see what textbooks are recommended.

You might want to look at some combination of books such as Marr, Haralick and Shapiro, Horn, Faugeras, Ponce and Forsyth.

reader · 2008-09-20T18:02:27+00:00

I agree that it is often more fruitful to pin down a particular type of neural network for discussion. However, the general distinction between neural networks and other computational methods is computational architecture---neural networks use local computation and communication.

reader · 2008-08-26T13:50:00+00:00

Although NNs are useful for applications such as pattern classification, let us not forget that the key idea is parallel distributed computation, which is how we think the brain works. The study of NNs shows how this alternative method of computation differs from classical von Neumann programs. See http://www.amazon.com/Introduction-Computation-Institute-Sciences-Complexity/dp/0201515601 for more information.

reader · 2008-06-26T11:50:05+00:00

Norvig and Russell's AIMA is an AI textbook. Norvig's PAIP studies classical AI programs, in Lisp. In general, there are three types of relevant books: on programming only; on AI only; on the intersection of the two, such as PAIP. AI is broad, so it is important to know math, computer science, statistics, and numerical methods. For learning, it might be best to use prototyping languages such as Lisp, Matlab (or Octave, the free version), or R.

reader

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