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Machine learning paradigm
In 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
Supervised_learning
Machine learning paradigm
Self-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
Self-supervised_learning
Paradigm in machine learning
Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the advent
Weak_supervision
Computational model used in machine learning
Zhai X, Oliver A, Kolesnikov A (October 2019). "S4L: Self-Supervised Semi-Supervised Learning". 2019 IEEE/CVF International Conference on Computer Vision
Neural network (machine learning)
Neural_network_(machine_learning)
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Set of learning techniques in machine learning
explicit algorithms. Feature learning can be either supervised, unsupervised, or self-supervised: In supervised feature learning, features are learned using
Representation_learning
Paradigm in machine learning that uses no classification labels
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled
Unsupervised_learning
Subset of artificial intelligence
technique. Semi-supervised learning falls between unsupervised learning (without any labelled training data) and supervised learning (with completely
Machine_learning
2018 text-generating language model
models primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets
GPT-1
Application of computer science in biology
are gene regulatory, protein interaction and metabolic networks. Supervised learning is a type of algorithm that learns from labeled data and learns how
Computational_biology
Type of large language model
(GP) was a long-established technique in machine learning. GP is a form of self-supervised learning wherein a model is first trained on a large, unlabeled
Generative pre-trained transformer
Generative_pre-trained_transformer
Machine learning technique
typically accomplished via supervised learning, but there are also techniques to fine-tune a model using weak supervision. Fine-tuning can be combined
Fine-tuning_(deep_learning)
Paradigm of rule-based machine learning methods
computation) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems
Learning_classifier_system
Academic conference in machine learning
International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the oldest
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Machine learning paradigm using minimal training data
rather than the large datasets required by conventional supervised learning. One-shot learning is the special case of the N-way K-shot framing in which
Few-shot_learning
Algorithm for supervised learning of binary classifiers
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether
Perceptron
Technique to make a model more generalizable and transferable
gather than input examples, semi-supervised learning can be useful. Regularizers have been designed to guide learning algorithms to learn models that respect
Regularization_(mathematics)
Algorithm for modelling sequential data
typically are first pretrained by self-supervised learning on a large generic dataset, followed by supervised fine-tuning on a small task-specific dataset
Transformer_(deep_learning)
Principle in artificial intelligence
Decoding With Self-Supervised Learning". Forty-second International Conference on Machine Learning. Proceedings of Machine Learning Research. Retrieved
Bitter_lesson
Machine learning technique
that TL would become the next driver of machine learning commercial success after supervised learning. In the 2020 paper, "Rethinking Pre-Training and
Transfer_learning
Concept in machine learning
invalidating the model) Data dredging Overfitting Resampling (statistics) Supervised learning Training, validation, and test sets Shachar Kaufman; Saharon Rosset;
Leakage_(machine_learning)
Machine learning technique
In machine learning, attention is a method that determines the importance of, or "attends to", each component in a sequence relative to the other components
Attention_(machine_learning)
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Academic conference in machine learning
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year.
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Data analysis techniques for fraud detection
The machine learning and artificial intelligence solutions may be classified into two categories: 'supervised' and 'unsupervised' learning. These methods
Data analysis for fraud detection
Data_analysis_for_fraud_detection
Theory of machine learning
Theoretical results in machine learning often focus on a type of inductive learning known as supervised learning. In supervised learning, an algorithm is provided
Computational_learning_theory
Machine learning methods using multiple input modalities
Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images
Multimodal_learning
Framework for machine learning
prediction. Learning falls into many categories, including supervised learning, unsupervised learning, online learning, and reinforcement learning. From the
Statistical_learning_theory
Type of feedforward neural network
visual scenes even when the objects are shifted. Several supervised and unsupervised learning algorithms have been proposed over the decades to train the
Convolutional_neural_network
Machine learning framework
unsupervised learning, GANs have also proved useful for semi-supervised learning, fully supervised learning, and reinforcement learning. The core idea
Generative adversarial network
Generative_adversarial_network
language models with many parameters, and are trained with self-supervised learning on a vast amount of text. For the training cost column, 1 petaFLOP-day
List_of_large_language_models
Overview of and topical guide to machine learning
computing Application of statistics Supervised learning, where the model is trained on labeled data Unsupervised learning, where the model tries to identify
Outline_of_machine_learning
Branch of machine learning
thousands) in the network. Methods used can be supervised, semi-supervised or unsupervised. Some common deep learning network architectures include fully connected
Deep_learning
Ensemble learning method
reducing bias. Boosting is a popular and effective technique used in supervised learning for both classification and regression tasks. The theoretical foundation
Boosting_(machine_learning)
Technique in machine learning
"CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images". arXiv:1808.01097 [cs.CV]. "Competence-based curriculum learning for neural machine
Curriculum_learning
Measurable property or characteristic
"Lecture 1: Supervised Learning". www.cs.cornell.edu. Retrieved 2026-09-09. Wittek, Peter (2014), "Quantum Computing", Quantum Machine Learning, Elsevier
Feature_(machine_learning)
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Method of machine learning
train the model. It represents a dynamic technique of supervised learning and unsupervised learning that can be applied when training data becomes available
Incremental_learning
Machine learning technique where agents learn from demonstrations
Imitation learning is a paradigm in reinforcement learning, where an agent learns to perform a task by supervised learning from expert demonstrations.
Imitation_learning
Machine learning technique
feedback, learning a reward model, and optimizing the policy. Compared to data collection for techniques like unsupervised or self-supervised learning, collecting
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Deep learning architecture
Mamba is a deep learning architecture focused on sequence modeling. It was developed by two researchers Albert Gu from Carnegie Mellon University and Tri
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Plot of machine learning model performance over time or experience
descent "Mohr, Felix and van Rijn, Jan N. "Learning Curves for Decision Making in Supervised Machine Learning - A Survey." arXiv preprint arXiv:2201.12150
Learning curve (machine learning)
Learning_curve_(machine_learning)
Overview of and topical guide to algorithms
Self-organizing map Reinforcement learning Q-learning State–action–reward–state–action (SARSA) Temporal difference learning Policy gradient method Actor–critic
Outline_of_algorithms
Type of statistical inference
In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases
Transduction (machine learning)
Transduction_(machine_learning)
datasets. High-quality labeled training datasets for supervised and semi-supervised machine-learning algorithms are usually difficult and expensive to produce
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Artificial neural network that mimics neurons
unsupervised biologically inspired learning methods are available such as Hebbian learning and STDP, no effective supervised training method is suitable for
Spiking_neural_network
Type of feedforward neural network
radial basis networks, another class of supervised neural network models). In recent developments of deep learning the rectified linear unit (ReLU) is more
Multilayer_perceptron
2023 text-generating language model
was trained using a combination of first supervised learning on a large dataset, then reinforcement learning using both human and AI feedback, it did
GPT-4
Tasks in machine learning
naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Reverse-engineering neural networks
(December 2023). "Emergent Linear Representations in World Models of Self-Supervised Sequence Models". Proceedings of the 6th BlackboxNLP Workshop: Analyzing
Mechanistic_interpretability
Method of speech synthesis that uses deep neural networks
self-supervised learning has gained much attention through better use of unlabelled data. Research has shown that, with the aid of self-supervised loss
Deep learning speech synthesis
Deep_learning_speech_synthesis
Supervised machine learning techniques
Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured
Structured_prediction
Machine learning calibration technique
Alexandru; Caruana, Rich (2005). Predicting good probabilities with supervised learning (PDF). ICML. doi:10.1145/1102351.1102430. Olivier Chapelle; Vladimir
Platt_scaling
Property of a model
prevent supervised learning algorithms from generalizing beyond their training set: The bias error is an error from erroneous assumptions in the learning algorithm
Bias–variance_tradeoff
Automated recognition of patterns and regularities in data
categorized according to the type of learning procedure used to generate the output value. Supervised learning assumes that a set of training data (the
Pattern_recognition
Intelligence in machines
machine learning: Unsupervised learning analyses a stream of data, finds patterns, and makes predictions without any other guidance. Supervised learning requires
Artificial_intelligence
Machine learning strategy
concept can often be much lower than the number required in normal supervised learning. However, there is a risk that the algorithm is overwhelmed by uninformative
Active learning (machine learning)
Active_learning_(machine_learning)
Flaw in mathematical modelling
overfitting occurs when a model begins to "memorize" training data rather than "learning" to generalize from a trend. As an extreme example, if the number of parameters
Overfitting
Concept in artificial intelligence
of learning by observing an expert. It can be viewed as a form of supervised learning, where the training dataset consists of task executions by a demonstration
Apprenticeship_learning
Method of machine learning
online learning paradigms for LLMs to enable continuous, real-time adaptation after the initial training. In the setting of supervised learning, a function
Online_machine_learning
Process of automating the application of machine learning
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination
Automated_machine_learning
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs
History of artificial neural networks
History_of_artificial_neural_networks
Use of artificial intelligence in the automation of electronic design
include supervised learning, unsupervised learning, reinforcement learning, and generative AI. Supervised learning is a type of machine learning where algorithms
AI-driven_design_automation
Class of artificial neural network
predictability in the incoming data sequence, the highest level RNN can use supervised learning to easily classify even deep sequences with long intervals between
Recurrent_neural_network
Machine learning model for vision processing
(2023-04-14). "DINOv2: Learning Robust Visual Features without Supervision". arXiv:2304.07193 [cs.CV]. "DINOv3: Self-supervised learning for vision at unprecedented
Vision_transformer
Class of artificial neural networks
passing" for such approaches. In the more general subject of "geometric deep learning", certain existing neural network architectures can be interpreted as GNNs
Graph_neural_network
Supervised learning of a similarity function
Similarity learning is an area of supervised machine learning in artificial intelligence. It is closely related to regression and classification, but the
Similarity_learning
Artificial neural network algorithm
machine learning: supervised learning, unsupervised learning, and reinforcement learning. A lot of the learning methods in machine learning work similar
Learning_rule
Problem setup in machine learning
the performance in a semi-supervised like manner (or transductive learning). Unlike standard generalization in machine learning, where classifiers are expected
Zero-shot_learning
Artificial intelligence model developed by TypeSafe AI
Reinforcement Learning for Calibrated Decisions (RLCD). The details are unpublished, though it is suspected to be a standard supervised learning algorithm
Jev_(AI_model)
Overview of and topical guide to deep learning
language model Supervised learning Unsupervised learning Self-supervised learning Semi-supervised learning Reinforcement learning Transfer learning Multitask
Outline_of_deep_learning
Machine learning that combines deep learning and reinforcement learning
an artificial neural network. Deep learning methods, often using supervised learning with labeled datasets, have been shown to solve tasks that involve
Deep_reinforcement_learning
Method in natural language processing
multi-lingual) corpora, also providing an early example of self-supervised learning of word embeddings. Word embeddings come in two different styles
Word_embedding
Set of methods for supervised statistical learning
In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that
Support_vector_machine
Type of artificial neural network
radial basis networks, another class of supervised neural network models). In recent developments of deep learning, the rectified linear unit (ReLU) is more
Feedforward_neural_network
Software program
Through Deep Visualization. Deep Learning Workshop, International Conference on Machine Learning (ICML) Deep Learning Workshop. arXiv:1506.06579. Olah
DeepDream
Statistics and machine learning technique
much more flexible structure to exist among those alternatives. Supervised learning algorithms search through a hypothesis space to find a suitable hypothesis
Ensemble_learning
Computer-based method for summarizing a text
text about machine learning, the unigram "learning" might co-occur with "machine", "supervised", "un-supervised", and "semi-supervised" in four different
Automatic_summarization
Type of machine learning model
like reinforcement learning from human feedback (RLHF) or constitutional AI. Instruction fine-tuning is a form of supervised learning used to teach LLMs
Large_language_model
Vector quantization algorithm minimizing the sum of squared deviations
relationship to the k-nearest neighbor classifier, a popular supervised machine learning technique for classification that is often confused with k-means
K-means_clustering
instructions, automated labeling methods, or adversarial attacks in supervised learning. Label noise can be roughly divided into random noise, where labels
Label_noise
Tree-based ensemble machine learning methods
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude
Random_forest
French-Slovak computer scientist and AI researcher
startup-advisory roles. His research spans machine learning, reinforcement learning, self-supervised learning and large language models. In 2014, he introduced
Michal_Valko
Approach in data analysis
anomalies, and the visualisation of data can also be improved. In supervised learning, removing the anomalous data from the dataset often results in a
Anomaly_detection
Deep learning generative model to encode data representation
designed for unsupervised learning, its effectiveness has been proven for semi-supervised learning and supervised learning. A variational autoencoder
Variational_autoencoder
Machine-learning and computational-neuroscience conference
Processing Systems (abbreviated as NeurIPS and formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Set of machine learning methods
learning algorithms have been developed for supervised, semi-supervised, as well as unsupervised learning. Most work has been done on the supervised learning
Multiple_kernel_learning
Smooth approximation of one-hot arg max
version as "softargmax". The term "softmax" is conventional in machine learning. Formally, instead of considering the arg max as a function with categorical
Softmax_function
Technique for shaping training datasets
regularization algorithms can extend supervised learning algorithms in semi-supervised learning and transductive learning settings, where unlabeled data are
Manifold_regularization
Model-free reinforcement learning algorithm
Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring
Q-learning
Similarity measure for number sequences
techniques. This normalised form distance is often used within many deep learning algorithms. In biology, there is a similar concept known as the Otsuka–Ochiai
Cosine_similarity
Type of activation function
performance without unsupervised pre-training, especially on large, purely supervised tasks. Advantages of ReLU include: Sparse activation: for example, in
Rectified_linear_unit
Extracting features from raw data for machine learning
In supervised machine learning and statistical modeling, feature engineering is a preprocessing step which transforms raw data into a more effective set
Feature_engineering
Machine learning technique
algorithm by plugging in a different loss and its gradient. Many supervised learning problems involve an output variable y and a vector of input variables
Gradient_boosting
Difficulties arising when analyzing data with many aspects ("dimensions")
techniques for classification (including the k-NN classifier), semi-supervised learning, and clustering, and it also affects information retrieval. In a
Curse_of_dimensionality
Memory unit used in neural networks
Bahdanau, Dzmitry; Bougares, Fethi; Schwenk, Holger; Bengio, Yoshua (2014). "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine
Gated_recurrent_unit
Framework for mathematical analysis of machine learning
computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed
Probably approximately correct learning
Probably_approximately_correct_learning
Type of supervised learning in machine learning
In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually
Multiple_instance_learning
Principle in statistical learning theory
"empirical risk". The following situation is a general setting of many supervised learning problems. There are two spaces of objects X and Y and we would like
Empirical_risk_minimization
Artificial neural network node function
significantly affect most of the weights. In the latter case, smaller learning rates are typically necessary.[citation needed] Continuously differentiable
Activation_function
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SUPERVISED LEARNING
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SUPERVISED LEARNING
SUPERVISED LEARNING
SUPERVISED LEARNING
SUPERVISED LEARNING
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