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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 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)
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
Algorithm for modelling sequential data
Vinh Q.; Garcia, Xavier; Wei, Jason; Wang, Xuezhi; Chung, Hyung Won; Shakeri, Siamak; Bahri, Dara (2023-02-28), UL2: Unifying Language Learning Paradigms
Transformer_(deep_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
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
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
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 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
Computational model used in machine learning
2025.103405. ISSN 2090-4479. Lv Z, Poiesi F, Dong Q, Lloret J, Song H (11 November 2022). "Deep Learning for Intelligent Human–Computer Interaction". Applied
Neural network (machine learning)
Neural_network_(machine_learning)
Overview of and topical guide to machine learning
Generalization Meta-learning Inductive bias Metadata Reinforcement learning Q-learning State–action–reward–state–action (SARSA) Temporal difference learning (TD) Learning
Outline_of_machine_learning
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)
Researcher in computational neuroscience
reinforcement learning (RL) where he and his colleagues proposed that dopamine signals reward prediction error, and helped develop the Q-learning algorithm
Peter_Dayan
Type of large language model
generative artificial intelligence chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre-trained on large datasets
Generative pre-trained transformer
Generative_pre-trained_transformer
Computer programming concept
consequences of pharmacological manipulations of dopamine on learning. PVLV Q-learning Rescorla–Wagner model State–action–reward–state–action (SARSA)
Temporal_difference_learning
Reinforcement learning algorithms
methods, and value-based RL algorithms such as value iteration, Q-learning, SARSA, and TD learning. An AC algorithm consists of two main components: an "actor"
Actor-critic_algorithm
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
Type of feedforward neural network
A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike
Convolutional_neural_network
Optimization algorithm
machine learning. Both statistical estimation and machine learning consider the problem of minimizing an objective function that has the form of a sum: Q (
Stochastic_gradient_descent
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
Smooth approximation of one-hot arg max
reinforcement learning, a softmax function can be used to convert values into action probabilities. The function commonly used is: P t ( a ) = exp ( q t ( a
Softmax_function
Type of machine learning model
performance via collaborative platforms such as Hugging Face. As machine learning algorithms process numbers rather than text, the text must be converted
Large_language_model
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
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
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
Type of database that uses vectors to represent other data
from the raw data using machine learning methods such as feature extraction algorithms, word embeddings or deep learning networks. The goal is that semantically
Vector_database
Topics referred to by the same term
Amazon Q, AI–powered assistant released in 2023 Q-learning, AI algorithm Q*, a rumored internal name for OpenAI o1 Q-telecom, Greek operator Motorola Q, smartphone
Q_(disambiguation)
Mathematical model for sequential decision making under uncertainty
array Q {\displaystyle Q} and uses experience to update it directly. This is known as Q-learning. Another application of MDP process in machine learning theory
Markov_decision_process
Seventeenth letter of the Latin alphabet
Q (minuscule: q) is the seventeenth letter of the Latin alphabet, used in the modern English alphabet, the alphabets of other western European languages
Q
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
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
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
Class of reinforcement learning algorithm
(MC) RL, SARSA, and Q-learning. Monte Carlo estimation is a central component of many model-free RL algorithms. The MC learning algorithm is essentially
Model-free (reinforcement learning)
Model-free_(reinforcement_learning)
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)
Mathematical problem in cryptography
learning with errors problem L W E q , ϕ {\displaystyle \mathrm {LWE} _{q,\phi }} is to find s ∈ Z q n {\displaystyle \mathbf {s} \in \mathbb {Z} _{q}^{n}}
Learning_with_errors
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)
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
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
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
Set of learning techniques in machine learning
In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations
Feature_learning
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
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
Machine learning algorithm
and Niranjan in a technical note with the name "Modified Connectionist Q-Learning" (MCQ-L). The alternative name SARSA, proposed by Rich Sutton, was only
State–action–reward–state–action
State–action–reward–state–action
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
2018 text-generating language model
primarily employed supervised learning from large amounts of manually labeled data. This reliance on supervised learning limited their use of datasets
GPT-1
Set of methods for supervised statistical learning
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms
Support_vector_machine
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
Streaming media technique
Multiple approaches have been presented in literature using the SARSA or Q-learning algorithm. In all of these approaches, the client state is modeled using
Adaptive_bitrate_streaming
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
Method used to normalize the range of independent variables
Q 2 ( x ) Q 3 ( x ) − Q 1 ( x ) {\displaystyle x'={\frac {x-Q_{2}(x)}{Q_{3}(x)-Q_{1}(x)}}} where Q 1 ( x ) , Q 2 ( x ) , Q 3 ( x ) {\displaystyle Q_{1}(x)
Feature_scaling
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)
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
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
page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of artificial
Timeline_of_machine_learning
Deep learning generative model to encode data representation
In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling in 2013
Variational_autoencoder
Type of activation function
silencing of the parts of the model found to be stimuli-irrelevant during learning that allows for scaling. As the stimuli-irrelevant proportion of the model
Rectified_linear_unit
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
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)
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; some
Pattern_recognition
2020 text-generating language model
of 2,048 tokens, and has demonstrated strong "zero-shot" and "few-shot" learning abilities on many tasks. On September 22, 2020, Microsoft announced that
GPT-3
Conversational software
would behave as a conversational partner. Such chatbots often use deep learning and natural language processing. Simpler chatbots have existed for decades
Chatbot
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
Reverse-engineering neural networks
identify structures, circuits or algorithms encoded in the weights of machine learning models. This contrasts with earlier interpretability methods that focused
Mechanistic_interpretability
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 model for vision processing
Machine Learning (PMLR). 139: 10096–10106. arXiv:2104.00298. Retrieved 31 October 2023. Huang, Gao; Liu, Zhuang; van der Maaten, Laurens; Q. Weinberger
Vision_transformer
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
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
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
Taiwanese multinational company
lighting, esports equipment, remote work and learning, wireless presentation, and other peripherals. BenQ's head office is in Taipei, Taiwan, and the company
BenQ
Type of artificial neural network
{\displaystyle \sigma _{2}} , p {\displaystyle p} and q {\displaystyle q} can be used and result in different learning algorithms for regression, classification,
Extreme_learning_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
Set of statistical processes for estimating the relationships among variables
(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
Method in machine learning
called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy
Bootstrap_aggregating
Machine learning calibration technique
machines" (PDF). Machine Learning. 68 (3): 267–276. doi:10.1007/s10994-007-5018-6. Guo, Chuan; Pleiss, Geoff; Sun, Yu; Weinberger, Kilian Q. (2017-07-17). "On
Platt_scaling
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
Method for discovering interesting relations between variables in databases
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended
Association_rule_learning
Type of artificial neural network
these early efforts did not lead to a working learning algorithm for hidden units, i.e., deep learning. In 1965, Alexey Grigorevich Ivakhnenko and Valentin
Feedforward_neural_network
Deep learning library
PyTorch is an open-source deep learning library, originally developed by Meta Platforms and currently developed with support from the Linux Foundation
PyTorch
Deep learning method
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence
Generative adversarial network
Generative_adversarial_network
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
Class of artificial neural network
whose middle layer contains recurrent connections that change by a Hebbian learning rule. Later, in Principles of Neurodynamics (1961), he described "closed-loop
Recurrent_neural_network
Set of machine learning methods
Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination
Multiple_kernel_learning
Recurrent neural network architecture
its advantage over other RNNs, hidden Markov models, and other sequence learning methods. It aims to provide a short-term memory for RNN that can last thousands
Long_short-term_memory
Reinforcement learning is used heavily in the field of machine learning and can be seen in methods such as Q-learning, policy search, Deep Q-networks and
Machine learning in video games
Machine_learning_in_video_games
intelligence and machine learning by devising techniques to improve reinforcement learning. He presented a deterministic Q-learning algorithm that uses distance
Atulya_Nagar
Optimization algorithm
methods for optimization. Gradient descent is particularly useful in machine learning and artificial intelligence for minimizing the cost or loss function. Gradient
Gradient_descent
Models used to produce word embeddings
Rong, Xin (5 June 2016), word2vec Parameter Learning Explained, arXiv:1411.2738 Hinton, Geoffrey E. "Learning distributed representations of concepts."
Word2vec
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)
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
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
Type of convolutional neural network
regression using U-Net and its application on pansharpening; 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation; TernausNet: U-Net
U-Net
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
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
Topics referred to by the same term
QANDA, an AI-based learning platform Comparison of Q&A sites This disambiguation page lists articles associated with the title Q&A. If an internal link
Q&A
Automatic creation of ontologies
Ontology learning (ontology extraction, ontology augmentation generation, ontology generation, or ontology acquisition) is the automatic or semi-automatic
Ontology_learning
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)
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)
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
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
Iterative method for finding maximum likelihood estimates in statistical models
F ( q , θ ) := E q [ log L ( θ ; x , Z ) ] + H ( q ) , {\displaystyle F(q,\theta ):=\operatorname {E} _{q}[\log L(\theta ;x,Z)]+H(q),} where q is an
Expectation–maximization algorithm
Expectation–maximization_algorithm
Q LEARNING
Q LEARNING
Boy/Male
Indian
The provider
Girl/Female
Tamil
Vidhyavathi | விதà¯à®¯à®¾à®µà®¾à®¤à¯€
Wisdom, Knowledge, Learning, Goddess Durga
Vidhyavathi | விதà¯à®¯à®¾à®µà®¾à®¤à¯€
Girl/Female
Tamil
Saraswathi | ஸரஸà¯à®µà®¾à®¤à¯€Â
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswathi | ஸரஸà¯à®µà®¾à®¤à¯€Â
Girl/Female
Tamil
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Knowledge, Learning
Vidhya | விதà¯à®¯à®¾,விதà¯à®¯à®¾Â
Girl/Female
Tamil
Vaagdevi | வாகà¯à®¤à¯‡à®µà¯€
Goddess of learning, Saraswati
Vaagdevi | வாகà¯à®¤à¯‡à®µà¯€
Girl/Female
Tamil
Goddess of learning, Saraswati
Surname or Lastname
English
English : topographic name for someone who lived by a gate or ‘hatch’ (especially one leading into a forest), northern Middle English heck (Old English hæcc), or a habitational name from Great Heck in North Yorkshire, which is named with this word. Compare Hatch.German : topographic name from Middle High German hecke, hegge ‘hedge’. This name is common in southern Germany and the Rhineland.Possibly an Americanized spelling of French Hec(q), a topographic name from Old French hec ‘gate’, ‘barrier’, ‘fence’ (compare 1), or a habitational name from a place named with this word.Shortened form of the Dutch surname van (den) Hecke, a habitational name from any of several places called ten Hekke in the Belgian provinces of East and West Flanders.
Girl/Female
Sikh
Knowledge, Learning
Girl/Female
Tamil
Saraswati | ஸரஸà¯à®µà®¤à¯€
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswati | ஸரஸà¯à®µà®¤à¯€
Girl/Female
Tamil
Learning
Boy/Male
Tamil
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Learning ocean
Vidaysagar | விதாயà¯à®¸à®¾à®•à®°
Boy/Male
Tamil
Vidyasagar | விதà¯à®¯à®¾à®¸à®¾à®•à®°Â
Ocean of learning
Vidyasagar | விதà¯à®¯à®¾à®¸à®¾à®•à®°Â
Girl/Female
Tamil
Sarasvati | ஸரஸà¯à®µà®¤à¯€
A Goddess of learning
Sarasvati | ஸரஸà¯à®µà®¤à¯€
Girl/Female
Tamil
Saraswathy | ஸரஸà¯à®µà®¾à®¤à¯€ Â
Goddess Saraswati, Tamil Goddess for education, Goddess of learning
Saraswathy | ஸரஸà¯à®µà®¾à®¤à¯€ Â
Boy/Male
Muslim
The provider
Girl/Female
Tamil
Goddess of learning, Goddess Saraswati
Surname or Lastname
English, French, German, Hungarian (Donát), Polish, and Czech (Donát)
English, French, German, Hungarian (Donát), Polish, and Czech (Donát) : from a medieval personal name (Latin Donatus, past participle of donare, frequentative of dare ‘to give’). The name was much favored by early Christians, either because the birth of a child was seen as a gift from God, or else because the child was in turn dedicated to God. The name was borne by various early saints, among them a 6th-century hermit of Sisteron and a 7th-century bishop of Besançon, all of whom contributed to the popularity of the baptismal name in the Middle Ages, which was not checked by the heresy of a 4th-century Carthaginian bishop who also bore it. Another bearer was a 4th-century gramMarian and commentator on Virgil, widely respected in the Middle Ages as a figure of great learning.
Girl/Female
Tamil
Goddess of learning, Saraswati
Girl/Female
Tamil
Vidyasri | விதà¯à®¯à®¾à®¸à®°à¯€
Wisdom, Knowledge, Learning, Goddess Durga
Vidyasri | விதà¯à®¯à®¾à®¸à®°à¯€
Girl/Female
Tamil
Goddess of learning, Saraswati
Q LEARNING
Q LEARNING
Girl/Female
Tamil
Arnika | à®…à®°à¯à®¨à®¿à®•à®¾
Goddess Durga
Girl/Female
Hindu, Indian
Name of a God
Female
English
Variant spelling of English Sierra, CIERRA means "mountain range."
Boy/Male
Assamese, Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Telugu
Attractive
Girl/Female
Hindu, Indian, Modern
Goddess Lakshmi; Money; Lucky
Girl/Female
French, German, Greek, Latin, Swedish
Pure
Boy/Male
Hindu
Boy/Male
Arabic, Hindu, Indian, Muslim
Of Reddish Hair or Complexion; Name of the First Roman to Embrace Islam
Girl/Female
Tamil
Fresh butter, Gentle, Soft, Always new
Boy/Male
Indian, Sanskrit
Lord Shiva
Q LEARNING
Q LEARNING
Q LEARNING
Q LEARNING
Q LEARNING
a.
Being without; destitute; free; wanting; devoid; as, void of learning, or of common use.
a.
Pertaining to, or suiting, a scholar, a school, or schools; scholarlike; as, scholastic manners or pride; scholastic learning.
n.
The character and qualities of a scholar; attainments in science or literature; erudition; learning.
a.
Not exhibiting learning; as, unlearned verses.
n.
The acetabulum. See Acetabulum, 2. Q () the seventeenth letter of the English alphabet, has but one sound (that of k), and is always followed by u, the two letters together being sounded like kw, except in some words in which the u is silent. See Guide to Pronunciation, / 249. Q is not found in Anglo-Saxon, cw being used instead of qu; as in cwic, quick; cwen, queen. The name (k/) is from the French ku, which is from the Latin name of the same letter; its form is from the Latin, which derived it, through a Greek alphabet, from the Ph/nician, the ultimate origin being Egyptian.
n.
One engaged in the pursuits of learning; a learned person; one versed in any branch, or in many branches, of knowledge; a person of high literary or scientific attainments; a savant.
q.
Moving or causing motion; motory; active, as opposed to latent.
n.
An institution organized and incorporated for the purpose of imparting instruction, examining students, and otherwise promoting education in the higher branches of literature, science, art, etc., empowered to confer degrees in the several arts and faculties, as in theology, law, medicine, music, etc. A university may exist without having any college connected with it, or it may consist of but one college, or it may comprise an assemblage of colleges established in any place, with professors for instructing students in the sciences and other branches of learning.
v. t.
To be without; to be destitute of, or deficient in; not to have; to lack; as, to want knowledge; to want judgment; to want learning; to want food and clothing.
n.
The acorn cup of two kinds of oak (Quercus macrolepis, and Q. vallonea) found in Eastern Europe. It contains abundance of tannin, and is much used by tanners and dyers.
n.
The knowledge or skill received by instruction or study; acquired knowledge or ideas in any branch of science or literature; erudition; literature; science; as, he is a man of great learning.
n.
A native or inhabitant of Byzantium, now Constantinople; sometimes, applied to an inhabitant of the modern city of Constantinople. C () C is the third letter of the English alphabet. It is from the Latin letter C, which in old Latin represented the sounds of k, and g (in go); its original value being the latter. In Anglo-Saxon words, or Old English before the Norman Conquest, it always has the sound of k. The Latin C was the same letter as the Greek /, /, and came from the Greek alphabet. The Greeks got it from the Ph/nicians. The English name of C is from the Latin name ce, and was derived, probably, through the French. Etymologically C is related to g, h, k, q, s (and other sibilant sounds). Examples of these relations are in L. acutus, E. acute, ague; E. acrid, eager, vinegar; L. cornu, E. horn; E. cat, kitten; E. coy, quiet; L. circare, OF. cerchier, E. search.
n.
Instruction in school; tuition; education in an institution of learning; act of teaching.
v. t.
To train in an institution of learning; to educate at a school; to teach.
n.
One of several American blackbirds, of the family Icteridae; as, the rusty grackle (Scolecophagus Carolinus); the boat-tailed grackle (see Boat-tail); the purple grackle (Quiscalus quiscula, or Q. versicolor). See Crow blackbird, under Crow.
n.
A book used in schools for learning lessons.
n.
A place for learned intercourse and instruction; an institution for learning; an educational establishment; a place for acquiring knowledge and mental training; as, the school of the prophets.
n.
A beginner in learning; one who is in the rudiments of any branch of study; a person imperfectly acquainted with a subject; a novice.
a.
Having the place of articulation on the soft palate; guttural; as, the velar consonants, such as k and hard q.
n.
The acquisition of knowledge or skill; as, the learning of languages; the learning of telegraphy.