In this post I will cover some basic machine learning definitions to help me understand things moving forward.

Problem Types

Classification

Here, the goal of the algorithm is to use information about a given item or data instance, and assign it a result or category (Laird, van Lent, 2005).

Clustering

Similar to classification, but the resulting categories are not pre-defined, and multiple data instances are used to define groups (Laird, van Lent, 2005).

Optimization

Modifying function input variables to find the highest or most optimal output result.

Algorithm Lingo

Decision Tree

A decision tree represents a process of classification through a mufti-step decision-making tree (SAS Institute).

Rules

With respect to machine learning, a rule is something that an system may define based on observations that defines how it should react to a given input.

Neural Network

Generally requires specific hardware. Neural networks learn by example rather than programming, by simulating a highly interconnected network similar to the human brain (Siganos , D and Stergiou, C).

Model

A model is a data set generated by the computer which is used to represent something. For example, a player model could be a set of variables that are set in order to represent the behaviours of a player.

References

Laird, John; van Lent, Michael. “Machine Learning for Computer Games.” Game Developers Conference. Moscone Center West, San Francisco, CA. 10 Mar. 2005. Lecture.

“Decision Trees— What Are They?.” . Statistical Analysis System Institute. Web. <http://support.sas.com/publishing/pubcat/chaps/57587.pdf>

Siganos , Dimitrios , and Stergiou, Christos . “Neural Networks.” Neural Networks. N.p., n.d. Web. 18 June 2014. <http://www.doc.ic.ac.uk/~nd/surprise_96/journal/vol4/cs11/report.html>.