Learning About Statistical Learning • Hacker News Board

Re: Learning About Statistical Learning

#41

Post by global-variable » Sat, Jan 16, 2010, 6:40 AM UTC

fgimenez wrote: post #34

justokay wrote: post #22

Mike Jordan at Berkeley sent me his list on what people should learn for ML. The list is definitely on the more rigorous side (ie aimed at more researchers than practitioners), but going through these books (along with the requisite programming experience) is a useful, if not painful, exercise. I personally think that everyone in machine learning should be (completely) familiar with essentially all of the material in…

Keep in mind that Mike Jordan is a superhuman math machine. I remember his undergraduate research assistants at Cal were telling me that it would take grad students days to understand 5 minute proofs he would do on the fly.

It's almost like he's the Michael Jordan of math.

Re: Learning About Statistical Learning

#42

Post by billswift » Sat, Jan 16, 2010, 11:55 AM UTC

tel wrote: post #19

I like his language toolbelt for this kind of work. At a minimum, I would recommend learning python (numpy/scipy), R, and at least one nice functional language (probably Haskell, Clojure, or OCaml). This is effectively what I use as well. Python as a general purpose data munging library that's good for all of your dirty work whenever you need it. R for graphing, graphing, graphing, running statistical tests other peo…

There is a website http://software-carpentry.org/ intended to teach scientists the basics of reliable programming. It's python based and covers version control, debugging, shells, testing, and the basics of databases and other stuff that would be useful for scientists without computer training. Since a lot of practical stuff like this isn't covered in any systematic way in most comp sci programs, it is useful even for people who have programming experience.

Re: Learning About Statistical Learning

#43

Post by herdrick » Sat, Jan 16, 2010, 5:32 PM UTC

brent wrote: post #29

Tichy wrote: post #4

Interesting on the one hand, but is anybody seriously going to go through those books one by one now? Personally I have troubles going through just one book (Pattern Recognition by Bishop atm), and even that might be useless without practical application. I managed to eventually read through MacKay (enjoyable book and available as a free PDF, too) and feel I have already forgotten most of it again :-( Another way mig…

My advice is not to try to read through these books (like Elements or Bishop's book). If I were to learn the topic from scratch again * I would: 1. Learn basic terminology (basically, skim the chapters and understand roughly what the topics are) 2. Work on a problem in depth. You are probably interested in a certain area or type of problem. a. Read the relevant chapters in detail. b. Pick up the necessary math along…

Great, great point. I think that's the way to learn anything.

Re: Learning About Statistical Learning

#44

Post by bradfordcross » Sat, Jan 16, 2010, 6:11 PM UTC

Perceval wrote: post #31

I just read yesterday a book on Non-parametric Statistics: http://www.amazon.com/gp/product/047045461X/ref=oss_T15_prod... I found this useful and interesting, because a great deal of social phenomena are not normally distributed (do not fit a Gaussian bell-curve distribution, regardless of the size of the sample). I am interested in learning more about non-parametric statistics, and statistics using alternative dist…

http://www.amazon.com/All-Nonparametric-Statistics-Springer-...