Hi everyone,
I am not sure whether this is the place to put this kind of stuff.

I would like to share a link with you. I am the Co-Founder of a website http://www.searching-eye.com/

Here I deliver lectures in Machine Learning, Convex Interpretation of Machine Learning algorithms, Reinforcement Learning and Kernel Methods in reinforcement learning, Path Planning and Convex Optimization.

Here is an online course for Advanced reinforcement learning -
ARL-10/11 : http://www.searching-eye.com/sanjeevsharma/co/arl/
Here I try to mix decentralization (primal & dual decomposition) and convex optimization in reinforcement learning and trying to produce applications in robotics and navigation.

Anyone can access the lectures (video), presentations (pdf) and Matlab code (for complex algorithms) for every lecture in ARL-10/11.

For a sample, I have included this link - Kernel Perceptron Learning - http://www.searching-eye.com/video/98/Machine-Learning-Perceptrons-Kernel-Perceptron-Learning and here's the link for all lectures I have delivered - http://www.searching-eye.com/sanjeevsharma/mpreslec.html
I think that this can be useful for some of you.

I am also looking for brilliant programmers to collaborate with searching-eye and build new applications. If you like, please reply personally, if you think this is a spam, please ignore, I again rephrase, I am not sure whether this is suitable place for such type of posts.
Thanks,

Sanjeev.
Undergraduate Student.

Good topics! It's cool that you've put this up.

Others may be interested in the following related links, which I heartily recommend:

- The book (free, PDF) Convex Optimization by Boyd and Vandenberghe is extremely good.

- Stephen Boyd's lectures for Convex Optimization I and Convex Optimization II are available online. If you want to learn convex optimization this is the place to go.

- The book (free, PDF) Planning Algorithms by Steve LaValle is a very good introduction to planning from a state-space perspective.

[Edited by - Emergent on November 17, 2010 8:33:40 PM]