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About this Blended Virtual Instructor Led Training (VILT)
“Let the data speak for itself” is something that many scientists mention when referring to Machine Learning (ML), indicating that hidden or latent relationships between observations and classes of (desired) outcomes can be derived using these algorithms.
Examples are in the field of seismic processing (first arrival picking), interpretation (facies prediction) and other related areas. Often, we resort to statistical relationships. Then Machine Learning enters into the game, from a range of labelled data (called instances), we can derive a linear/nonlinear relationship (model in ML terminology) that predicts the label (supervised learning) of new data (instances in ML terminology). But sometimes, it is already useful if an algorithm can define separate clusters, which then still need to be interpreted (unsupervised learning). Even more sophisticated is semi-supervised learning: labelled and unlabelled data together are clustered whereby the unlabelled data then receives the label of the dominant class present in the cluster.
