This page is under construction.
Maths for Machine Learning
This Wiki Site is basically somewhere where we can maintain a list of required maths for machine
learning research. Basically if you are probably going to need to use it sometime, it should go in here.
The aim is to collate stuff at this stage. Then we will sort it and work through it. Though possibly unlikely, we may end up
sorting this out and publishing it. So at the moment anything posted may be used by someone else to aid or as
part of a publication that is not in your name. Basically if you want to maintain copyright don't put it on here.
Realistically though that is unlikely to be an issue. Really a list of things we think people need to know
(at all levels) would be useful.
e.g.
How to integrate scalar fields over submanifolds, and hence how to deduce the probability density that is
induced by a higher dimensional distribution on a conditioned submanifold -- P(s|P(x),x=f(s)) -- for s,x vectors in R^m,R^n, m<n. See
http://www.math.duke.edu/~wka/math204/intman.pdf. Avoid the usual 3D restriction stuff.
Basic vector calculus without the physics: vector calculus without any focus on 3 Dimensions (mainly
ignore cross product stuff). But possibly with 3D examples to help with intuition.
Differentiation of vectors matrices wrt vectors matrices etc. The use of summation convention and a systematic overview of shortcuts. A quick introduction to mathematica for this purpose.
An optimization handbook. Different types of optimization scenarios, and pointers to methods for solution without going into
gory details (e.g. Interior point versus other methods). Basically what method set to use when. Pointers to actual working implementations.
Site Tools of the ANC Web
Notes:
- You are currently in the ANC web. The color code for this web is this background, so you know where you are.
- If you are not familiar with the TWiki collaboration platform, please visit WelcomeGuest first.
--
AmosStorkey - 02 Nov 2007
Topic revision: r2 - 02 Nov 2007 - 14:38:53 -
AmosStorkey