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Learning Equals Representation Evaluation Optimization
The field of machine learning has exploded in recent years and researchers have developed an enormous number of algorithms to choose from. Despite this great variety of models to choose from, they can all be distilled into three components.
The three components that make a machine learning model are representation, evaluation, and optimization. These three are most directly related to supervised learning, but it can be related to unsupervised learning as well.
Representation - this describes how you want to look at your data. Sometimes you may want to think of your data in terms of individuals (like in k-nearest neighbors) or like in a graph (like in Bayesian networks).
Evaluation - for supervised learning purposes, you'll need to evaluate or put a score on how well your learner is doing so it can improve. This evaluation is done using an evaulation function (also known as an objective function or scoring function). Examples include accuracy and squared error.
Optimization - using the evaluation function from above, you need to find the learner with the best score from this evaluation function using a choice of optimization technique. Examples are a greedy search and gradient descent.