Editing Theoretical computer science (section)

===Machine learning===
{{main|Machine learning}}
[[Machine learning]] is a [[academic disciplines|scientific discipline]] that deals with the construction and study of [[algorithm]]s that can [[learning|learn]] from data.<ref>{{cite journal |title=Glossary of terms |author1=Ron Kovahi |author2=Foster Provost |journal=[[Machine Learning (journal)|Machine Learning]] |volume=30 |pages=271–274 |year=1998 |url=https://ai.stanford.edu/~ronnyk/glossary.html|doi=10.1023/A:1007411609915 |doi-access=free }}</ref> Such algorithms operate by building a [[Statistical model|model]] based on inputs<ref name="bishop">{{cite book |author=C. M. Bishop |author-link=Christopher M. Bishop |year=2006 |title=Pattern Recognition and Machine Learning |publisher=Springer |isbn=978-0-387-31073-2}}</ref>{{rp|2}} and using that to make predictions or decisions, rather than following only explicitly programmed instructions.

Machine learning can be considered a subfield of computer science and [[statistics]]. It has strong ties to [[artificial intelligence]] and [[mathematical optimization|optimization]], which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based [[algorithm]]s is infeasible. Example applications include [[spam filter]]ing, [[optical character recognition]] (OCR),<ref name=Wernick-Signal-Proc-July-2010>Wernick, Yang, Brankov, Yourganov and Strother, Machine Learning in Medical Imaging, ''[[IEEE Signal Processing Society|IEEE Signal Processing Magazine]]'', vol. 27, no. 4, July 2010, pp. 25–38</ref> [[Learning to rank|search engines]] and [[computer vision]]. Machine learning is sometimes conflated with [[data mining]],<ref>{{cite conference |last=Mannila |first=Heikki |title=Data mining: machine learning, statistics, and databases |conference=Int'l Conf. Scientific and Statistical Database Management |publisher=IEEE Computer Society |year=1996}}</ref> although that focuses more on exploratory data analysis.<ref>{{cite journal |last=Friedman |first=Jerome H. |author-link=Jerome H. Friedman |title=Data Mining and Statistics: What's the connection? |journal=Computing Science and Statistics |volume=29 |issue=1 |year=1998 |pages=3–9}}</ref> Machine learning and [[pattern recognition]] "can be viewed as two facets of 
the same field."<ref name="bishop"/>{{rp|vii}}