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.