Figure 1.

Overview of the types of machine learning and algorithms. Only the most commonly used algorithms are described in this tutorial. AdaBoost, adaptive boosting; DBSCAN, density‐based spatial clustering of applications with noise; DCNN, deep convolutional neural networks; Eclat, equivalence class transformation; FP‐Growth, frequent pattern growth; GRU, gated recurrent unit; K‐NN, K‐nearest neighbors; LDA, linear discriminant analysis; LightGBM, light gradient boosting machine; LSA, latent semantic analysis; LSM, liquid state machine; LSTM, long short‐term memory; MLP, multilayer perceptron; PCA, principal component analysis; seq2seq, sequence‐to‐sequence; SVD, singular value decomposition; SVM, support vector machine; t‐SNE, t‐distributed stochastic neighbor embedding; XGBoost, extreme gradient boosting.