Machine Learning Models

A neural network is a group of interconnected units called neurons that send signals to one another.

A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization.

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where the order of elements is important.

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.

In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that analyze data for classification and regression analysis.

A Markov decision process (MDP) is a mathematical model for sequential decision making when outcomes are uncertain.

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).