An agreed upon definition of machine learning is, a computer program is said to have learned when it's performance measure P at task T improves with experience E. Under the definition of Supervised Learning, we get this diagram. Here the experience would be the training data required to improve the algorithm. In practice we put this data into the Design Matrix. Design Matrix [dəˈzīn ˈmātriks]: term -- if a single input can be represented as a vector, putting all of the training examples, i.e the vectors, into 1 matrix makes the entire input aspects of the training data. This is not all of the experience.
Jan-17-2020, 20:38:25 GMT