| If you already have a little exposure to machine learing, let me recomend an interesting review paper [1] on random forests: http://research.microsoft.com/pubs/155552/decisionForests_MS... It isn't everything you need know in 30 minutes, but it's a concrete coverage of lots of topics in machine learning in under 150 pages. Here's why I'm recomending this paper: * The algoritm is easy to understand. * It can handle classification, regression, semi-supervised learning, manifold learning, and density estimation. The paper gives an introduction to each of these topics as well as a unified framework to implement each algorithm. * It can handle categorical data and missing data [2] * It gives as good results as other state of the art algorithms. * The paper is well-written and easy to understand for someone without a deep background in machine learning. [1] It's mostly a review paper. Using random forests for density estimation is new. [2] This review paper doesn't cover categorical data or missing data. |
Is another great resource that introduces many ML topics from the ground up.