(Statistics|Probability|Machine Learning|Data Mining|Data and Knowledge Discovery|Pattern Recognition|Data Science|Data Analysis)

1 - What Is

The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope.

Machine Learning is the common term for supervised learning methods and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision.

Data Science - History

?? TODO: Splits between proba/stat en machine leraning ?

It used to be that we [computer scientists] studied discrete math, now we study probability and statistics John Hopcroft heidelberg laureate forum 2014

I keep saying that the sexy job in the next 10 years will be statisticians.“ Hal Varian, Chief Economist at Google (New York Times in 2009).
In the article, there's a picture of Carrie Grimes, who was a graduate from Stanford Statistics. She was one of the first statisticians hired at Google.

My personal statistical paradigm I use statistical models, which are sets of equations involving random variables, with associated distributional assumptions, devised in the context of a question and a body of data concerning some phenomenon, with which tentative answers can be derived, along with measures of uncertainty concerning these answers.

<MATH> \begin{array}{rcl} questions + data & \rightarrow & answers + \text{measures of uncertainty} \\ & \underbrace{}_{\displaystyle \text{equations, distributions}} & \\ \end{array} </MATH> Terry Speed

3 - Domain Application

4 - Group

5 - Documentation / Reference

data_mining/start.txt · Last modified: 2017/09/13 16:04 by gerardnico