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[Thrun] So this finishes my short lecture on robotics.

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Obviously the field is much, much bigger than what I just showed you,

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but I gave you examples of the key elements.

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I gave you an example of perception using particle filters.

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I gave you an example of planning using MDPs and also A*.

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These are some of the key methods we've been applying to self-driving cars.

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There are many other methods.

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Most notably, there's also reinforcement learning

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that has recently received a lot of attention.

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But I don't have time to talk about those.

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I hope you are able to apply these methods yourself to pretty much any robotics problems

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that you might be working on.

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Robotics is really a fascinating area.

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There's a lot of things to learn--

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way too much than I can offer in this single class.

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But what you should have noticed is there's a really strong interplay

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between artificial intelligence and the methods I showed you before

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and what we are doing, for example, to make cars drive themselves.

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Now, in the next class we'll talk about a topic that's equally important,

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which is natural language processing.

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So when you learned today a little bit about how to build self-driving cars,

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next lecture you might actually learn how to build the next Google

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when Peter Norvig tells you all about natural language processing.

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So please come and see us again when the next class comes up.