ML – Some Basics

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

Problem Types


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).


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


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).


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).


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.


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. <>

Siganos , Dimitrios , and Stergiou, Christos . “Neural Networks.” Neural Networks. N.p., n.d. Web. 18 June 2014. <>.

Leave a Reply

Your email address will not be published. Required fields are marked *