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Overview of and topical guide to machine learning
The following outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence
Outline_of_machine_learning
Subset of artificial intelligence
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn
Machine_learning
Overview of and topical guide to deep learning
The following outline is provided as an overview of, and topical guide to, deep learning: Deep learning is a subfield of machine learning and artificial
Outline_of_deep_learning
Academic conference in machine learning
The International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Academic conference in machine learning
with NeurIPS and ICML, it is one of the three primary conferences of highest impact and reputation in machine learning and artificial intelligence research
International Conference on Learning Representations
International_Conference_on_Learning_Representations
Machine learning technique
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence
Attention_(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 of automation
Automated_machine_learning
Ensemble learning method
In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single
Boosting_(machine_learning)
Concept in machine learning
In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that
Leakage_(machine_learning)
are a comparison of machine learning software such as software frameworks, libraries, and computer programs used for machine learning. Apache OpenNLP —
Comparison of machine learning software
Comparison_of_machine_learning_software
Plot of machine learning model performance over time or experience
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and
Learning curve (machine learning)
Learning_curve_(machine_learning)
Research field that lies at the intersection of machine learning and computer security
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques
Adversarial_machine_learning
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which input data
Transformer_(deep_learning)
Machine learning calibration technique
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution
Platt_scaling
Machine learning strategy
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)
Active learning (machine learning)
Active_learning_(machine_learning)
Machine learning technique
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related
Transfer_learning
Measurable property or characteristic
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating
Feature_(machine_learning)
Deep learning architecture
Through Structured State Space Duality". Proceedings of the 41st International Conference on Machine Learning. PMLR: 10041–10071. Bergmann, Dave (2025-07-07)
Mamba (deep learning architecture)
Mamba_(deep_learning_architecture)
Type of large language model
the concept of generative pre-training (GP) was a long-established technique in machine learning. GP is a form of self-supervised learning wherein a model
Generative pre-trained transformer
Generative_pre-trained_transformer
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
Machine learning technique
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization
Normalization (machine learning)
Normalization_(machine_learning)
Statistics and machine learning technique
and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the
Ensemble_learning
AI that learns decision rules from data
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves
Rule-based_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
Class of artificial neural network
for machine translation, while another 2014 study demonstrated sequence-to-sequence learning using LSTMs. They became state of the art in machine translation
Recurrent_neural_network
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
learning – Weak supervision (semi-supervised learning) – Unsupervised learning – Natural language processing (outline) – Chatterbots – Language identification
Outline of artificial intelligence
Outline_of_artificial_intelligence
Type of artificial neural network
learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with
Extreme_learning_machine
Academic journal
The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the
Journal of Machine Learning Research
Journal_of_Machine_Learning_Research
2018 text-generating language model
employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets that were
GPT-1
Type of feedforward neural network
network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep learning network has been applied
Convolutional_neural_network
Machine-learning and computational-neuroscience conference
formerly NIPS) is a machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary
Conference on Neural Information Processing Systems
Conference_on_Neural_Information_Processing_Systems
Machine learning paradigm using minimal training data
Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small
Few-shot_learning
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
Machine learning technique
Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous
Mixture_of_experts
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
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Type of feedforward neural network
In deep learning, a multilayer perceptron (MLP) is a kind of modern feedforward neural network consisting of fully connected neurons with nonlinear activation
Multilayer_perceptron
Reverse-engineering neural networks
identify structures, circuits or algorithms encoded in the weights of machine learning models. This contrasts with earlier interpretability methods that
Mechanistic_interpretability
Theory of machine learning
computational learning theory (or just learning theory) is a subfield of artificial intelligence devoted to studying the design and analysis of machine learning algorithms
Computational_learning_theory
Integrated circuit technology
digital, or mixed-mode VLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing
Neuromorphic_computing
Research field in deep learning
topological information of datasets to make them available for traditional machine-learning techniques, such as support vector machines or random forests.
Topological_deep_learning
Machine learning method
Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching
Logic_learning_machine
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
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
Similarity measure for number sequences
California: Lifetime Learning Publications. p. 149. Thomson, Godfrey (1916). "A hierarchy without a general factor" (PDF). British Journal of Psychology. 8:
Cosine_similarity
Smooth approximation of one-hot arg max
"softmax" is conventional in machine learning. This section uses the term "softargmax" for clarity. Formally, instead of considering the arg max as a
Softmax_function
2023 text-generating language model
called reinforcement learning from human feedback, which trains the model to refuse prompts which go against OpenAI's definition of harmful behavior, such
GPT-4
Memory unit used in neural networks
Bengio, Yoshua (2014). "Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation". Proceedings of the 2014 Conference
Gated_recurrent_unit
Technique in machine learning
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"
Curriculum_learning
Method of machine learning
In computer science, incremental learning is a method of machine learning in which input data is continuously used to extend the existing model's knowledge
Incremental_learning
Type of database that uses vectors to represent other data
audio, and other types of data, can all be vectorized. These feature vectors may be computed from the raw data using machine learning methods such as feature
Vector_database
Type of machine learning model
neural theory of language (NTL) as a computational basis for using language as a model of learning tasks and understanding. The NTL model outlines how specific
Large_language_model
Framework for machine learning
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory
Statistical_learning_theory
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
Method in natural language processing
Globerson, Amir (2007). "Euclidean Embedding of Co-occurrence Data" (PDF). Journal of Machine Learning Research. Qureshi, M. Atif; Greene, Derek (2018-06-04)
Word_embedding
Tuning parameter (hyperparameter) in optimization
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration
Learning_rate
Problem setup in machine learning
Zero-shot learning (ZSL) is a problem setup in machine learning where, at test time, a learner observes samples from classes which were not observed during
Zero-shot_learning
Tree-based ensemble machine learning methods
decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training
Random_forest
Flaw in mathematical modelling
Bousquet, Olivier (2011-09-30), "The Tradeoffs of Large-Scale Learning", Optimization for Machine Learning, The MIT Press, pp. 351–368, doi:10.7551/mitpress/8996
Overfitting
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
Machine learning that combines deep learning and reinforcement learning
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
Deep_reinforcement_learning
Type of activation function
units improve restricted boltzmann machines." Proceedings of the 27th international conference on machine learning (ICML-10). 2010. Vaswani, A. (2017)
Rectified_linear_unit
Method of machine learning
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update
Online_machine_learning
Type of artificial neural network
model". The Journal of Machine Learning Research. 3: 1137–1155. Auer, Peter; Harald Burgsteiner; Wolfgang Maass (2008). "A learning rule for very simple
Feedforward_neural_network
Machine learning technique
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional
Gradient_boosting
2019 text-generating language model
exaggerated; Anima Anandkumar, a professor at Caltech and director of machine learning research at Nvidia, said that there was no evidence that GPT-2 had
GPT-2
Model-free reinforcement learning algorithm
policy optimization". Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37. ICML'15. Lille, France:
Proximal_policy_optimization
Programming paradigm
programming has found use in a wide variety of areas, particularly scientific computing and machine learning. One of the early proposals to adopt such a framework
Differentiable_programming
Type of artificial neural network
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple
Deep_belief_network
Computer programming concept
difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value
Temporal_difference_learning
Numerical method that reduces the complexity of computationally intensive simulations
to this end, the method is also associated with the field of machine learning. The main use of POD is to decompose a physical field (like pressure, temperature
Proper orthogonal decomposition
Proper_orthogonal_decomposition
Machine learning model training problem
In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered
Vanishing_gradient_problem
Software program
Visualizing Higher-Layer Features of a Deep Network. International Conference on Machine Learning Workshop on Learning Feature Hierarchies. S2CID 15127402
DeepDream
Problem in machine learning and statistical classification
In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into
Multiclass_classification
Tasks in machine learning
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function
Training, validation, and test data sets
Training,_validation,_and_test_data_sets
Statistical model of language
Christian (2003). "A Neural Probabilistic Language Model". Journal of Machine Learning Research. 3: 1137–1155. Mikolov, Tomáš; Karafiát, Martin; Burget
Language_model
Set of statistical processes for estimating the relationships among variables
variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more independent variables (often called regressors
Regression_analysis
Type of artificial intelligence system
language models (LLMs), which are limited to text. It is an example of multimodal learning. Many widely used commercial applications now rely on this ability
Vision-language_model
Software user interface
context of machine learning.It is also used in conversational AI to manage complex interactions that require human empathy. In machine learning, HITL is
Human-in-the-loop
Branch of machine learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
Conversational software
decades, but chatbots based on deep learning have gained popularity during the AI boom of the 2020s, with the releases of generative AI (GenAI) chatbots such
Chatbot
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
Statistical Markov model
Ultimate Learning Machine Will Remake Our World. Basic Books. p. 37. ISBN 978-0-465-06192-1. Kundu, Amlan, Yang He, and Paramvir Bahl. "Recognition of handwritten
Hidden_Markov_model
Method used to normalize the range of independent variables
the data preprocessing step. Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work
Feature_scaling
Optimization algorithm
statistical estimation and machine learning consider the problem of minimizing an objective function that has the form of a sum: Q ( w ) = 1 n ∑ i = 1
Stochastic_gradient_descent
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 due
K-means_clustering
Machine-learning process
in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or
Grammar_induction
Set of learning techniques in machine learning
In machine learning (ML), representation learning or feature learning is a set of techniques that allow a system to automatically discover the representations
Representation_learning
(ANNs) are models created using machine learning to perform a number of tasks. While the computational implementations of ANNs relate to earlier discoveries
History of artificial neural networks
History_of_artificial_neural_networks
Artificial neural network node function
Power of Over-parametrization in Neural Networks with Quadratic Activation". Proceedings of the 35th International Conference on Machine Learning. PMLR:
Activation_function
Recurrent neural network architecture
Markov models, and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands of timesteps (thus "long short-term
Long_short-term_memory
Subfield of machine learning
Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of 2017
Meta-learning (computer science)
Meta-learning_(computer_science)
Artificial intelligence algorithm
A Tsetlin machine is an artificial intelligence algorithm based on propositional logic. A Tsetlin machine is a form of learning automaton collective for
Tsetlin_machine
Machine learning-powered structure design
technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS has been used to design networks
Neural_architecture_search
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
Use of software programs to generate taxonomical classifications from a body of texts
taxonomy learning Outline automation Outline building Outline construction Outline creation Outline extraction Outline generation Outline induction Outline learning
Automatic taxonomy construction
Automatic_taxonomy_construction
Automated recognition of patterns and regularities in data
recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power. Pattern recognition
Pattern_recognition
Machine learning framework
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. The concept
Generative adversarial network
Generative_adversarial_network
Machine learning problem
In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution
Probabilistic_classification
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OUTLINE OF-MACHINE-LEARNING
OUTLINE OF-MACHINE-LEARNING
Girl/Female
Bengali, Indian
Machine
Female
German
German form of Scottish Malvina, MALWINE means "smooth-brow."
Female
English
Feminine form of English Max, MAXINE means either "the greatest rival" or "the stream of Mack."Â
Male
Hebrew
Variant spelling of Hebrew Yakiyn, YACHIN means "he establishes" or "whom God strengthens."Â
Female
French
French feminine form of Latin Martinus, MARTINE means "of/like Mars."Â
Girl/Female
Australian, Japanese
Child of Machi
Female
Scottish
Feminine form of Scottish Lachlan, LACHINA means "lake-land."
Male
French
French form of Latin Macarius, MACAIRE means "blessed."
Boy/Male
American, Australian
Weighing Machine
Female
German
Pet form of German Ottilia, OTTOLINE means "wealthy."
Surname or Lastname
English
English : variant spelling of Machen.Spanish (MachÃn) : probably a nickname from machÃn ‘boor’, ‘lout’, often applied to a blacksmith’s apprentice.French : nickname from Old French machin ‘scheming’.
Female
Native American
Native American Hopi name KACHINA means "sacred dancer; spirit."
Male
English
Pet form of English Sacheverell, SACHIE means "roe-buck leap."
Female
French
Feminine form of French Marin, MARINE means "of the sea."
Female
Yiddish
(×™Ö·×—Ö°× Ö¶×¢) Yiddish form of Hebrew Yochana, YACHNE means "God is gracious."Â
Male
English
Variant spelling of English unisex Macey, MACIE means "gift of God."
Female
English
Variant spelling of English Maureen, MAURINE means "obstinacy, rebelliousness" or "their rebellion."
Female
English
Elaborated form of English Opal, OPALINE means "gem, precious stone."
Female
Hawaiian
Hawaiian name MAHINA means "moon; moonlight."
Male
Scottish
Pet form of Scottish Gaelic Lachlann, LACHIE means "lake-land."
OUTLINE OF-MACHINE-LEARNING
OUTLINE OF-MACHINE-LEARNING
OUTLINE OF-MACHINE-LEARNING
OUTLINE OF-MACHINE-LEARNING
OUTLINE OF-MACHINE-LEARNING
OUTLINE OF-MACHINE-LEARNING
OUTLINE OF-MACHINE-LEARNING
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