Machine learningMachine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to…View analysis →
Supervised learningIn machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based on example…View analysis →
Unsupervised learningUnsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Other frameworks in…View analysis →
Multi-agent reinforcement learningMulti-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist in a shared…View analysis →
Quantum machine learningQuantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks which analyze classical…View analysis →
Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational…View analysis →
Neural network (machine learning)In machine learning, a neural network (NN) or neural net, is a computational model inspired by the structure and functions of biological neural networks.View analysis →
Reinforcement learningIn machine learning and optimal control, reinforcement learning (RL)View analysis →
Self-supervised learningSelf-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals, rather than relying on…View analysis →
