Day #1(6/22): Hundred-Page Machine Learning Book p.1–10

Supervised: A collection of labeled examples which includes feature vectors that describe the example and the label that can belong to classes, real number or more sophisticated vector, matrix, tree, or a graph. It is to produce a model that will take feature vector and input and output a label that describes the collection of that particular feature vectors. Unsupervised: dataset collection of unlabeled examples, where x is a feature vector as input and'either transforms it into another vector or into a value that can be used to solve a practical problem'. It can be used for clustering, the model returns the id of the cluster for each feature vector in the dataset. It can be used for dimensionality reduction, where the output of the model is a feature vector that has a fewer features than the input x. Finally, the output is for outlier detection where its a real number that shows how x is different from other typical data points.