Machine Learning Intro
Machine Learning is the instrument to “learn” models from data (i.e. inferring Bayes Networks from the available data). Machine Learning algorithms/approaches could be identified by following aspects.
How they defined: Parameters, Structure, Hidden concepts
How they learn: Supervised, Unsupervised, Reinforcement learning
What they are used for: Prediction, Diagnostics, Summarization, etc.
How learning is done: Passive, Active, Online, Offline.
Outputs: Regression Vs. Classification