I completed the Machine Learning course (Stanford) and got very interested, also after some research, I decided that I'd like to learn nature-inspired algorithms.

I've found some resources like:

The first reference look good and complete with pseudocode (giving me the possibility to implement everything in Ruby, my prefered language), and also gives ruby implementations for every code. But it lacks of exercises to practice, which I think is a key feature.

The second is something that inspired me a lot to start studying this area, but they don't have any course or material to study.

The third one looks good too, but has only a little amount of exercises, and might be too focused (I do like games, but I also want to study everything else related to nature-inspired algorithms). Also it is focused on C++ (not that it is a hard language, but I don't like its limitation compared to ruby), and I'd prefere something in Ruby or pseudocode (although it is not my main priority).

Does anyone know something that also have exercises to complement the theory? Is there anything better to learn, with a particular focus on exercises? (Maybe courses or video lectures).

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  • I also took the ML course with Ng, great course and highly recommended! – JonnyBoats Jan 20 '12 at 4:26
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    I've always found it to be a good exercise to design and build a practical app that uses algorithms I want to learn. For example, when I was recently working with Bayesian algorithms I wrote a document classifier. – jfrankcarr Jan 20 '12 at 11:49
  • Agree, but I'll still need the theory and an idea for an app. – Rodrigo Ruiz Jan 20 '12 at 14:58
  • Take the exercises from the 3rd book and use them for practice when you're reading the first book ;) – alfasin Feb 12 '12 at 11:49

Here is a treasure trove of leads for you. In particular, I would recommend this book to you. The author, Xin-She Yang, knows what he is talking about and the book is relatively cheap.

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