Basics of algorithmics, computation models, formal languages
published: July 2, 2007, recorded: July 2007, views: 19950
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Part 1 1:01:07
Part 2 56:35
Part 3 24:14
Description
Between the many theoretical computer science issues that one should be aware of when working in Machine learning, we visit, in this series of lectures, two.
The first corresponds to strings, and through the study of strings, the questions about more complex structures like trees and graphs. We describe the main algorithmic and combinatorial questions about substrings and subsequences, and concentrate our attention to the topological questions: ordering strings and computing distances and kernels.
The second is complexity. Not only should we be aware (and have a reasonable control of the techniques involved) of the usual barriers, but we should know something about classes for randomized algorithms. We also show some examples concerning Las Vegas and Monte Carlo techniques.
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bootcamp07_higuera_bacm.pdf (522.0 KB)
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bootcamp07_higuera_bacm.ppt (723.0 KB)
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Reviews and comments:
A comment upon the talk. The idea was to start imagining some elements of TCS revisited for ML students. I suppose there are other possible ways of doing this, and I don't claim this is the most important bits nor that it is essential.
I also wish to say that there are some murky bits, specially about the KMP algorithm. (sorry)
I really enjoyed this lecture. Its the one that has most stayed with me. I knew a little about complexity classes, but you introduced a lot of incredible new stuff, quickly but it a very intuitive way.
I do really enjoyed this lecture. I like the way you deliver explanation, thus make me stayed until the end of the vid lecture. Thanks!
la partie 2 s'arrete et ne bouge plus après 18 minutes 52!
(the part 2 stops and do not want to continue when reaching 18 minutes 52!)
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