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cryptographic hash functions are basically fundamental building blocks that are used within many cryptographic algorithms amp amp protocols we have a number of very important applications in the context of information security as a whole now, some of the more common algorithms in this category, that are known as cryptographic hash functions, include things like MD5, amp amp also it has some predecessors like MD4 amp amp others as well as algorithms like

learning algorithms.

learning algorithms. There are several different types of learning algorithms.

And if these algorithms,

These are robust algorithms.

First, the detection algorithms.

So, why study algorithms?

In some cases, there would actually be algorithms, and very sophisticated algorithms.

Evolve biomorphs using genetic algorithms

Now, why are algorithms even important?

Now on to the detection algorithms.

For me, algorithms have several components.

There is a lot of very sort of specific algorithms that network analysts use, everything from very simple breadth first search algorithms to community detection algorithms.

And then we used some algorithms that are a lot like speech recognition algorithms, called pattern recognition.

Machine learning algorithms for data mining tasks

The bulk of our research is algorithms.

So, we've looked at 2 search algorithms.

Genetic algorithms are great for certain things

Computer Algorithms by Aho, Hopcroft and Ullman.

These algorithms could be written onto paper.

It turns out that clustering algorithms and Unsupervised Learning algorithms are used in many other problems as well.

That's always your skill in applying this algorithms.

And that is how we compare different algorithms.

For that we used artificial evolution genetic algorithms.

Some algorithms are really straightforward, but other algorithms take advantage of subtle mathematical properties to quickly and accurately provide an answer.

So what you can really think cryptographic hash functions as, is perhaps maybe the mathematical equivalent analog of a meat grinder.

How did we design the algorithms to do this?

That is, algorithms but viewing with big data sets.

These are two algorithms competing for your living room.

But here's the thing. Algorithms are rules. They're binary.

And the question is, are each of these algorithms complete?

learning algorithms, if you're able to take advantage of these

By now, you see the range of different learning algorithms.

And we're learning from neural nets, genetic algorithms, evolutionary computing.

Are these based on new scientific discoveries or new algorithms?

We made this maze really hard to test our algorithms.

The nice thing is these algorithms work almost real time.

We'll also going to look at approximation algorithms which are key tools for applying algorithms to a very large data seqs like these social networks.

So the curves in this figure corresponds to different algorithms and the way we compare different algorithms is by measuring the area and the curve.

The last thing I want to do on this slide is give you a sense of why these new and old algorithms are sort of the same thing or why they're both similar algorithms or why they're both gradient descent algorithms.

For example, you may have heard of something like a hash function used to form a hash table but the kind of hash functions that are used in hash tables and I wanna stress this, they aren't necessarily the same as cryptographic hash functions the qualifier cryptographic here is very, very important.

So you can sort of get your learning algorithms working quickly.

Now, let's consider the frontier for each of these search algorithms.

There's a couple well known algorithms textbooks that cover those details.