I took computational investing on coursera, which seems to have some overlap with the contents of the book. Course involves building and back testing stock trading strategies in python.
I'm doing Computational Investing from gatech on coursera now, https://www.coursera.org/course/compinvesting1 .
I do enjoy the course because I wanted to learn more about finance, although they could improve a lot on their presentation skills and material preparation. I hope they will take a lesson or two from their coursera feedback for the benefit of their online students.
renting machines really close to multiple exchanges and keeping them running would be cost prohibitive for most individual investor.
I'm currently taking https://www.coursera.org/course/compinvesting1 , it's quite basic but the next course, 'Computational Investing 2' will go through Machine Learning for investing (scheduled for January I think).
Hope this helps.
Cool - which course is she taking? This is the Computational Investing course. Buried very deep in a forum is a post by the tutor saying that you should know programming and how to use the command line - unfortunately it's too well hidden.
The main struggle has actually been with people on OSX Lion getting the libraries installed.
Not a problem. You are going to want to tackle your fundamentals first.
This includes things like linear algebra and basic matrix operations, statistical inference (correlation, p tests, sampling,probability,..), and then start working your way up the tree for algorithms.
The order in  is a good order and will get you familiar with the fundamentals in a fairly easy way. Use  for the overviews of different topics as well. Note that  isn't a good in depth practice though.
It's a fairly hand holding introduction to machine learning in general. After you get the brief overview down, start looking in to different problems that might interest you.
Depending on how you learn, since you know R, put some things in to practice with R. It has fantastic support for most learning algorithms as direct libraries.
Other than that, depending on what you're interested in (finance, so I'm assuming forecasting?) start looking in to domain specific problems that you're comfortable with.
One thing I've seen people do is use kaggle  for the "Knowledge" competitions. Those are essentially tutorials with test datasets.
Since you're in finance, you maybe interested in the signal processing based math that goes in to guessing when something happens within a certain time period.
See  for finance specific and  for the more general engineering math.
I hope that helps. Independent study is largely based on how you learn and just leveraging resources available coupled with practice in subjects you find interesting to maintain motivation.
Now keep in mind everyone is different. Some like the courses and homework approach, others just like experimenting on something they're interested in to accomplish concrete goals.
gedrapThe reason for sharing is that I've noticed many people asking about quant trading and etc recently. Hopefully it will be useful.
This could be an interesting course for people wanting to know more about 'computational investing' and the algorithms behind stock (market) analysis and trading: https://www.coursera.org/course/compinvesting1 . Not specially targeted at HFT, but interesting basic background information nevertheless.
An interesting course on using software algorithms for stock analysis and trading can be found at Coursera: https://www.coursera.org/course/compinvesting1
That course also uses Python as programming language in the examples. Short description from that URL: "Find out how modern electronic markets work, why stock prices change in the ways they do, and how computation can help our understanding of them. Build algorithms and visualizations to inform investing practice."
There is a coursera course called "Computational Investing, Part I" that I am taking that aims to build a market trading simulator to test a trading model. It just started so it's not too late to join.