fundamentals of machine learning four branches of machine learning in our previous examplesyou've become familiar with three specific types of machine-learning problemsbinary classificationmulticlass classificationand scalar regression all three are instances of supervised learningwhere the goal is to learn the relationship between training inputs and training targets supervised learning is just the tip of the iceberg--machine learning is vast field with complex subfield taxonomy machine-learning algorithms generally fall into four broad categoriesdescribed in the following sections supervised learning this is by far the most common case it consists of learning to map input data to known targets (also called annotations)given set of examples (often annotated by humansall four examples you've encountered in this book so far were canonical examples of supervised learning generallyalmost all applications of deep learning that are in the spotlight these days belong in this categorysuch as optical character recognitionspeech recognitionimage classificationand language translation although supervised learning mostly consists of classification and regressionthere are more exotic variants as wellincluding the following (with examples)sequence generation--given picturepredict caption describing it sequence generation can sometimes be reformulated as series of classification problems (such as repeatedly predicting word or token in sequencesyntax tree prediction--given sentencepredict its decomposition into syntax tree object detection--given picturedraw bounding box around certain objects inside the picture this can also be expressed as classification problem (given many candidate bounding boxesclassify the contents of each oneor as joint classification and regression problemwhere the bounding-box coordinates are predicted via vector regression image segmentation--given picturedraw pixel-level mask on specific object unsupervised learning this branch of machine learning consists of finding interesting transformations of the input data without the help of any targetsfor the purposes of data visualizationdata compressionor data denoisingor to better understand the correlations present in the data at hand unsupervised learning is the bread and butter of data analyticsand it' often necessary step in better understanding dataset before attempting to solve supervised-learning problem dimensionality reduction and clustering are well-known categories of unsupervised learning self-supervised learning this is specific instance of supervised learningbut it' different enough that it deserves its own category self-supervised learning is supervised learning without licensed to