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Could you give me some clarification about data mining and artificial intelligence algorithms? What mathematics base they used for? Could you give me starting point, in mathematics, to understand these types of algorithms?

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To give an example, David Ferrucci, who led IBM DeepQA/Watson's win on Jeopardy, said he it was destined to be a hybrid system: a team of 20-25 people for 4 years from multiple disciplines, including NLP, computational linguistics, game theory, stochastics and optimization and other disciplines worked on it. – alancalvitti Aug 17 '12 at 13:42
(Migration-related comments deleted) – mbq♦ Aug 17 '12 at 18:58
Top 10 algorithms in data mining gives a gentle overview of inspiring and leading algorithms. I'm afraid you'll need to provide more details (what applications? what level of details?) to get useful answers. – chl♦ Aug 17 '12 at 21:12

migrated from scicomp.stackexchange.com Aug 17 '12 at 18:56

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That can actually sound a little strange within community of statisticians, but I am pretty sure that most of machine learning algorithms can be formulated as a functional minimization problems. That means that this is going to be covered with mathematical optimization.

The other thing is that you will probably need calculus and linear algebra to understand what is optimization. And to interpret your results you will better have some background in probability theory and statistics.

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Is this purely a community of statisticians, is there a better stack exchange site for machine learning people, I'm not sure there is a dedicated one? – image_doctor Aug 18 '12 at 9:26
I don't know specific machine learning stack exchange site. But in this you can find a lot of "machine learning" people (for example me), as statistics and machine learning are really very-very connected. – Dmitry Laptev Aug 18 '12 at 11:09
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This question is maybe to broad, you should say something more about what you will use data mining for! But, data mining is essentially statistics, and much of the use of AI that I have seen is statistics as well. So, what math you need is the math you need for statistics: 1) calculus and real analysis 2) probability 3) Linear algebra! In practical terms, 3) may be the most important, almost whatever you will be doing (inclusive uses of 1) and 2)) you will depend heavily on linear algebra. So, be sure to get, not only the concepts, but manipulative skill!

A lot more is used, but maybe more specialized. So it does't make sense to give more detailed advice until you have specialized your question (and learnt 1), 2) & 3))

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It seems a fair question, what mathematics should I learn as a foundation for machine learning?
Maybe it is the answer that is broad. As ML draws from so many disciplines.

Others have suggested, Linear Algebra, Probability Theory, Satistics, Metric Spaces and many others which are all relevant.

Perhaps a workable approach is to list some of the most popular ML algorithms take a look at them and fill in the mathematics you feel you are less comfortable with.

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