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Subfield of machine learning, intelligent control, and control theory
Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems
Machine_learning_control
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
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)
Overview of and topical guide to machine learning
outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer
Outline_of_machine_learning
page is a timeline of machine learning. Major discoveries, achievements, milestones, and other major events in machine learning are included. History
Timeline_of_machine_learning
Interdisciplinary research area
Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks
Quantum_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
intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control, procedural
Machine learning in video games
Machine_learning_in_video_games
Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) (also known as approximate dynamic programming) is concerned with how an intelligent
Reinforcement_learning
Measure of required computing power
amount of computing power or computational resources required to train machine learning and large language models. The term "compute" has also been more broadly
Compute_(machine_learning)
Measurement of algorithmic bias
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions
Fairness_(machine_learning)
Decentralized machine learning
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients)
Federated_learning
Parameter controlling the machine learning process
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters
Hyperparameter (machine learning)
Hyperparameter_(machine_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
Artificial intelligence control techniques
control Machine learning control Reinforcement learning Bayesian control Fuzzy control Neuro-fuzzy control Expert Systems Genetic control New control
Intelligent_control
Statistics and machine learning technique
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from
Ensemble_learning
American mechanical engineer
where his research focuses on applying machine learning to dynamical systems, fluid mechanics, and control theory. He serves as Director of NSF AI Institute
Steven_L._Brunton
Computational model used in machine learning
In machine learning, a neural network (NN) or artificial neural network (ANN) is a computational model inspired by the structure and functions of biological
Neural network (machine learning)
Neural_network_(machine_learning)
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
Tuning parameter (hyperparameter) in optimization
which a machine learning model "learns". In the adaptive control literature, the learning rate is commonly referred to as gain. In setting a learning rate
Learning_rate
Model-free reinforcement learning algorithm
off-policy learning control with function approximation in Proceedings of the 27th International Conference on Machine Learning" (PDF). pp. 719–726.
Q-learning
Distance from a data point to a decision boundary
In machine learning, the margin of a data point is a measure of its separation from a classifier's decision boundary. A common distinction is made between
Margin_(machine_learning)
Software for understanding biological data
Machine learning in biology is the application of machine learning algorithms to biology, especially in computational biology and bioinformatics, including
Machine_learning_in_biology
German physicist
is closed-loop flow control for transport systems. Focus is placed on machine learning control and model-based nonlinear control using reduced-order modelling
Bernd_Noack
Machine learning for robots
Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills
Robot_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
Applications of machine learning to quantum physics
Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example
Machine_learning_in_physics
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
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
Computer scientist and professor
robotics, machine learning, and control. His research has centered on reinforcement learning, imitation learning, and scalable robot learning systems.
Sergey_Levine
2020 non-fiction book by Brian Christian
The Alignment Problem: Machine Learning and Human Values is a 2020 non-fiction book by the American writer Brian Christian. It is based on numerous interviews
The_Alignment_Problem
machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Branch of statistics
Wayback Machine." NIPS. 2010. Lopez-Paz, David, et al. "Towards a learning theory of cause-effect inference Archived 13 March 2017 at the Wayback Machine" ICML
Causal_inference
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
Hypothesis in machine learning
In machine learning, the lottery ticket hypothesis (LTH) is that artificial neural networks with random weights can contain a subnetwork which (entirely
Lottery_ticket_hypothesis
Method of machine learning
parameter or assumption that controls the relevancy of old data, while others, called stable incremental machine learning algorithms, learn representations
Incremental_learning
Optimization algorithm
become an important optimization method in machine learning. Both statistical estimation and machine learning consider the problem of minimizing an objective
Stochastic_gradient_descent
Intelligence in machines
develops and studies methods and software enabling machines to perceive their environment and use learning and intelligence to take actions that maximise
Artificial_intelligence
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem
Deep_reinforcement_learning
Process of finding the optimal set of variables for a machine learning algorithm
In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter
Hyperparameter_optimization
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
Artificial intelligence technique
Human Machine Learning (RHML) is an interdisciplinary approach to designing human-AI interaction systems. RHML aims to enable continual learning between
Reciprocal human machine learning
Reciprocal_human_machine_learning
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
Aspect of robotics
Force control is the control of the force with which a machine or the manipulator of a robot acts on an object or its environment. By controlling the contact
Force_control
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
Weak_supervision
Process of acquiring new knowledge
humans, other animals, and some machines. There is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single
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
Reinforcement learning method
named entity recognition (NER), machine translation (MT), speech recognition (SR), and dialogue systems. Error-driven learning models are ones that rely on
Error-driven_learning
Overview of and topical guide to deep learning
as an overview of, and topical guide to, deep learning: Deep learning is a subfield of machine learning and artificial intelligence based on artificial
Outline_of_deep_learning
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
Meta-learning (computer science)
Meta-learning_(computer_science)
Uruguayan electrical engineer and control theorist
His research spans robust control, system identification, semi-algebraic optimization, dynamics-enabled machine learning, and dynamic computer vision
Mario_Sznaier
American chemical engineer and ML researcher
process control, dynamic optimization, and machine learning applications in engineering. Hedengren has published on physics-informed machine learning for
John_D._Hedengren
AI whose outputs can be understood by humans
overlapping with interpretable AI, interpretable machine learning and explainable machine learning (XML), is a field of research that explores methods
Explainable artificial intelligence
Explainable_artificial_intelligence
American AI entrepreneur (born 1983)
worked on machine learning at Baidu and Google Brain. At OpenAI, he worked on the development of GPT-2, GPT-3, and reinforcement learning from human
Dario_Amodei
Machine learning algorithm
A learning automaton is one type of machine learning algorithm studied since 1970s. Learning automata select their current action based on past experiences
Learning_automaton
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
Collecting and analyzing telemetry from AI systems in production
of observability, which comes from control theory and software engineering, to systems based on machine learning models, and in particular to large language
AI_observability
Machine learning technique
Random features (RF) are a technique used in machine learning to approximate kernel methods, introduced by Ali Rahimi and Ben Recht in their 2007 paper
Random_feature
Means by which a user interacts with and controls a machine
and machines occur. The goal of this interaction is to allow effective operation and control of the machine from the human end, while the machine simultaneously
User_interface
Data analysis technique
analysis, and the technique is widely used in machine learning to reduce overfitting when training machine learning models, achieved by training models on several
Data_augmentation
Categorization of data using statistics
are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables
Statistical_classification
of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification. Machine learning is
Machine learning in earth sciences
Machine_learning_in_earth_sciences
In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers
Kernel_perceptron
Concept in machine learning
Double descent in statistics and machine learning is the phenomenon where a model's error rate on the test set initially decreases with the number of parameters
Double_descent
Machine learning audio synthesizer
Neural Audio Synthesis". Magenta. 6 April 2017. "NSynth Dataset". Machine Learning Datasets. Retrieved 2022-11-08. Ramires, António; Serra, Xavier (2019)
NSynth
Computer scientist
contributions to reinforcement learning and artificial intelligence". Sutton introduced temporal-difference methods for prediction and control, establishing convergence
Richard_S._Sutton
Model-free reinforcement learning algorithm
Proximal policy optimization (PPO) is a reinforcement learning (RL) algorithm for training an intelligent agent. Specifically, it is a policy gradient
Proximal_policy_optimization
Field of study in artificial intelligence
Machine unlearning is a branch of machine learning focused on removing specific undesired element, such as private data, wrong or manipulated training
Machine_unlearning
American machine learning company
platform allows users to share datasets and machine learning models. It also develops open-source machine learning libraries, notably the Transformers library
Hugging_Face
Engineering discipline
more theoretical fields such as electromagnetism, machine learning, artificial intelligence, control theory, signal processing, and microelectronics, and
Information_engineering
Property of a model
In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions
Bias–variance_tradeoff
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 was
Generative adversarial network
Generative_adversarial_network
platforms, and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
Class of artificial neural network
for machine learning algorithms, written in C and Lua. Applications of recurrent neural networks include: Machine translation Robot control Time series
Recurrent_neural_network
versioning, metadata management, and access control that help keep data quality and governance high. Machine learning Feature engineering Data pipeline Data
Feature_store
Educational mechanical device
type of machine which used his ideas on how learning should be directed with positive reinforcement. Skinner advocated the use of teaching machines for a
Teaching_machine
Spanish machine learning researcher
Oriol Vinyals (born 1983) is a Spanish machine learning researcher. He has developed most of his career at DeepMind. He is a co-founder of Discovery Loop
Oriol_Vinyals
Class of algorithms for pattern analysis
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These
Kernel_method
Open source version system
a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. It is designed to make ML models shareable
Data Version Control (software)
Data_Version_Control_(software)
Process where AI repeatedly improves itself
potentially surpass human control or understanding. In 1950, Alan Turing proposed the idea of developing a "child machine" that would be educated, rather
Recursive_self-improvement
Recurrent neural network architecture
series analysis tasks, speech recognition, machine translation, speech activity detection, robot control, video games, healthcare, energy forecasting
Long_short-term_memory
Type of machine learning method
to be confused with the lazy learning regime, see Neural tangent kernel). In machine learning, lazy learning is a learning method in which generalization
Lazy_learning
Biegel, Tobias; Metternich, Joachim (2021). "Machine Learning For Intelligent Maintenance And Quality Control: A Review Of Existing Datasets And Corresponding
Artificial intelligence in industry
Artificial_intelligence_in_industry
Machine learning researcher and CTO
Llion Jones is a Welsh machine learning researcher, co-founder and CTO of Sakana AI. Jones was born 1984 in Bangor, raised in Bangor and Abergynolwyn in
Llion_Jones
Framework for analyzing machine learning algorithms
Algorithmic learning theory is a mathematical framework for analyzing machine learning problems and algorithms. Synonyms include formal learning theory and
Algorithmic_learning_theory
British-American computer scientist
Memory-based Learning for Robot Control, was supervised by William F. Clocksin and examined machine learning techniques applied to robot control. Following
Andrew_W._Moore
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
Small autonomous underwater vehicle (AUV)
2000s with an accuracy rate of about 65%. After training COTSBot with machine learning, its accuracy rate rose to 99% by 2019. COTSBot is capable of killing
COTSBot
Type of machine learning model
A large language model (LLM) is a machine learning model (typically a neural network) trained on a vast amount of text for natural language processing
Large_language_model
Automated recognition of patterns and regularities in data
retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering;
Pattern_recognition
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
1948 book written by Norbert Wiener
Cybernetics: Or Control and Communication in the Animal and the Machine is a book written by Norbert Wiener and published in 1948. It is the first public
Cybernetics: Or Control and Communication in the Animal and the Machine
Cybernetics:_Or_Control_and_Communication_in_the_Animal_and_the_Machine
Concept in control theory
Reinforcement learning and optimal control are two closely related frameworks used to address sequential decision making problems; optimal control originates
Sequential_decision_making
Machine learning software library
MindSpore is an open-source software framework for deep learning, machine learning and artificial intelligence developed by Huawei. MindSpore provides
MindSpore
Machine learning researcher at Berkeley
for his cutting-edge research in robotics and machine learning, particularly in deep reinforcement learning. In 2021, he joined AIX Ventures as an Investment
Pieter_Abbeel
Field of scientific study
allow lifelong and open-ended learning of new skills and new knowledge in embodied machines. As in human children, learning is expected to be cumulative
Developmental_robotics
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
List of concepts in artificial intelligence
against a reigning world champion under regular time controls. deep learning A subset of machine learning that focuses on utilizing neural networks to perform
Glossary of artificial intelligence
Glossary_of_artificial_intelligence
Sub-field of reinforcement learning
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that coexist
Multi-agent reinforcement learning
Multi-agent_reinforcement_learning
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MACHINE LEARNING-CONTROL
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