Hi Kate,

Some of these nature primers might help you understand concept more (some statistical concepts also included):

• What is a support vector machine?
• What is a hidden Markov model?
• What is Bayesian statistics?

you can find full list here

I hope this helps.

update: These papers will give you a general overview of machine learning in biology and bioinformatics

• Machine Learning and Its Applications to Biology

This tutorial is structured in four main components. Firstly, a brief section reviews definitions and mathematical prerequisites. Secondly, the field of supervised learning is described. Thirdly, methods of unsupervised learning are reviewed. Finally, a section reviews methods and examples as implemented in the open source data analysis and visualization language R

• Machine learning in bioinformatics (2006)

You can then read and learn about the specific topics from the paper and references mentioned.

Listed because I'm a fan of their work, and they are also in close proximity to me.

(Edited to note that all of the work listed below is largely concerned with teasing out different elements of transcriptional regulation -- not so much evolutionary biology, which I missed you having mentioned in your question).

• Dana Pe'er does nice work. An integrated approach to uncover drivers of cancer is one particular example, which is an extension of Module Networks.
• Olivier Elemento. His work on FIRE is rather digestible and I think can make for a good journal club/presentation choice.
• Gunnar Raetsch has lots of papers to pick from -- these are typically algorithm heavy. They're quite good, but if your audience is very "green" to machine learning, it might be difficult to present -- especially if you aren't very familiar with the material.
• Christina Leslie's work (disclaimer: she's my advisor, so I'll keep this one last so it doesn't seem like I'm pushing it too hard ;-). The more ML-heavy works are the ones with her listed as last author. A recent ML-driven paper could be: Inferring transcriptional and microRNA-mediated regulatory programs in glioblastoma

You might check out the Weka project. I found it very useful when I was first learning about machine learning. And, they have almost 20 years worth of publications, making use of various techniques across many disciplines (including biology).