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Personalized pupil record
Learning Logs are a personalized learning resource for children. In the learning logs, the children record their responses to learning challenges set by
Learning_log
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
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
Relationship between proficiency and experience
measuring the strength of learning. It is usually expressed as n = log ( ϕ ) / log ( 2 ) {\displaystyle n=\log(\phi )/\log(2)} , where ϕ {\displaystyle
Learning_curve
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
Smooth approximation to the maximum function
by machine learning algorithms. It is defined as the logarithm of the sum of the exponentials of the arguments: L S E ( x 1 , … , x n ) = log ( exp
LogSumExp
Model of algorithmic learning
In computational learning theory, Occam learning is a model of algorithmic learning where the objective of the learner is to output a succinct representation
Occam_learning
Probability distribution
In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally
Log-normal_distribution
Education technology specification by IMS Global Learning Consortium
requiring a learner to log in separately on the external systems. The LTI will also share learner information and the learning context shared by the LMS
Learning Tools Interoperability
Learning_Tools_Interoperability
Mathematical function, inverse of an exponential function
formula: log b x = log 10 x log 10 b = log e x log e b . {\displaystyle \log _{b}x={\frac {\log _{10}x}{\log _{10}b}}={\frac {\log _{e}x}{\log _{e}b}}
Logarithm
Average uncertainty in variable's states
is H ( X ) := − ∑ x ∈ X p ( x ) log p ( x ) , {\displaystyle \mathrm {H} (X):=-\sum _{x\in {\mathcal {X}}}p(x)\log p(x),} where Σ {\displaystyle \Sigma
Entropy_(information_theory)
Information-theoretic measure
defined as follows: H ( p , q ) = − E p [ log q ] , {\displaystyle H(p,q)=-\operatorname {E} _{p}[\log q],} where E p [ ⋅ ] {\displaystyle \operatorname
Cross-entropy
Mathematical model
A log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model
Log-linear_model
Machine learning method to transfer knowledge from a large model to a smaller one
In machine learning, knowledge distillation or model distillation is the process of transferring knowledge from a large model to a smaller one. While large
Knowledge_distillation
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
Concept in machine learning
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price
Loss functions for classification
Loss_functions_for_classification
Function in statistics
log-odds function is the quantile function associated with the standard logistic distribution. It has many uses in data analysis and machine learning
Logit
Type of machine learning model
("Chinchilla scaling") for LLM autoregressively trained for one epoch, with a log-log learning rate schedule, states that: { C = C 0 N D L = A N α + B D β + L 0 {\displaystyle
Large_language_model
Statistical model for a binary dependent variable
a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent variables
Logistic_regression
Generalist medical doctor working in primary care
discussions, critique of videoed consultations and reflective entries into a "learning log". In addition, many hold qualifications such as the DCH (Diploma in Child
General_practitioner
Book by A. S. Neill
A.S. Neill's A Dominie's Log is a diary of his first year as headteacher at Gretna Green Village School, during 1914–15. It is an autobiographical novel
A_Dominie's_Log
Quantum algorithm for integer factorization
{\displaystyle O\!\left((\log N)^{2}(\log \log N)(\log \log \log N)\right)} using fast multiplication, or even O ( ( log N ) 2 ( log log N ) ) {\displaystyle
Shor's_algorithm
Concept in information theory
written as P P ( p ) = b − ∑ x p ( x ) log b p ( x ) , {\displaystyle \mathrm {PP} (p)=b^{-\sum _{x}p(x)\log _{b}p(x)},} where the value of b does not
Perplexity
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)
In computer log management and intelligence, log analysis (or system and network log analysis) is an art and science seeking to make sense of computer-generated
Log_analysis
Measure of ranking quality
e l i log 2 ( i + 1 ) = r e l 1 + ∑ i = 2 p r e l i log 2 ( i + 1 ) {\displaystyle \mathrm {DCG_{p}} =\sum _{i=1}^{p}{\frac {rel_{i}}{\log _{2}(i+1)}}=rel_{1}+\sum
Discounted_cumulative_gain
y} . In the learning augmented algorithm, probing the positions i + 1 , i + 2 , i + 4 , … {\displaystyle i+1,i+2,i+4,\ldots } takes log 2 ( η ) {\displaystyle
Learning_augmented_algorithm
Technique used in stochastic gradient variational inference
"reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic
Reparameterization_trick
Iterative method for finding maximum likelihood estimates in statistical models
expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a
Expectation–maximization algorithm
Expectation–maximization_algorithm
Overview of and topical guide to machine learning
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
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
Quantity in information theory
thought of as an alternative way of expressing probability, much like odds or log-odds, but which has particular mathematical advantages in the setting of
Information_content
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
Notion in supervised machine learning
set) is given by: Pr ( test error ⩽ training error + 1 N [ D ( log ( 2 N D ) + 1 ) − log ( η 4 ) ] ) = 1 − η , {\displaystyle \Pr \left({\text{test
Vapnik–Chervonenkis_dimension
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
Relationship between experience producing a good and the efficiency of that production
production (learning rate). To see this, note the following: C 2 x = C 1 ( 2 x ) log 2 ( b ) = C 1 x log 2 ( b ) ⋅ 2 log 2 ( b ) = C x ⋅ 2 log 2 ( b
Experience_curve_effect
Particular case of the generalized extreme value distribution
(also known as the Fisher–Tippett distribution). It is also known as the log-Weibull distribution and the double exponential distribution (a term that
Gumbel_distribution
Grouping a set of objects by similarity
retrieval, bioinformatics, data compression, computer graphics and machine learning. Cluster analysis refers to a family of algorithms and tasks rather than
Cluster_analysis
Educational hand-held game console
game and allows games to log user data, such as topics learned or user-created art. Logged activity is sent to LeapFrog's "Learning Path" system, which tracks
Leapster
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
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
Method of machine learning
improved further to a O ( log T ) {\displaystyle O(\log T)} for strongly convex and exp-concave loss functions. Continual learning means constantly improving
Online_machine_learning
meet the harm threshold. It can also be used as part of a GP trainee's learning log. The value of using SEA was highlighted in the publication of the GP
Significant_event_audit
The distributional learning theory or learning of probability distribution is a framework in computational learning theory. It has been proposed from Michael
Distribution_learning_theory
Probabilistic classification algorithm
when expressed in log-space: log p ( C k ∣ x ) ∝ log ( p ( C k ) ∏ i = 1 n p k i x i ) = log p ( C k ) + ∑ i = 1 n x i ⋅ log p k i = b + w k ⊤
Naive_Bayes_classifier
Measure of error in statistics
( log 0.9 + log 0.4 + log 0.7 + log 0.8 + log 0.4 + log 0.3 ) = 3.72 {\displaystyle -(\log 0.9+\log 0.4+\log 0.7+\log 0.8+\log 0.4+\log 0
Negative log predictive density
Negative_log_predictive_density
Concept in artificial intelligence
intelligence, apprenticeship learning (or learning from demonstration or imitation learning) is the process of learning by observing an expert. It can
Apprenticeship_learning
Time to make a decision as a result of the possible choices
choose among the choices is approximately: T = b ⋅ log 2 ( n + 1 ) {\displaystyle T=b\cdot \log _{2}(n+1)} where b is a constant that can be determined
Hick's_law
Academic journal
Sciences Interdisciplinary Public Service Sustainability Teaching and Learning Innovation Global Management ASU Prep Casa Grande Digital Phoenix High
Jurimetrics_(journal)
Mathematical problem in cryptography
In cryptography, learning with errors (LWE) is a mathematical problem that is widely used to create secure encryption algorithms. It is based on the idea
Learning_with_errors
Structuring text as input to generative artificial intelligence
in-context learning is temporary. Training models to perform in-context learning can be viewed as a form of meta-learning, or "learning to learn". Research
Prompt_engineering
Mathematical statistics distance measure
Q ) = ∑ x ∈ X P ( x ) log P ( x ) Q ( x ) . {\displaystyle D_{\text{KL}}(P\parallel Q)=\sum _{x\in {\mathcal {X}}}P(x)\,\log {\frac {P(x)}{Q(x)}}{\text{
Kullback–Leibler_divergence
Market town in Norfolk, England
March 2018). "Exercise 3.5: Local History". Bob Coe's OCA Landscape Learning Log. Retrieved 17 October 2019.{{cite web}}: CS1 maint: numeric names: authors
Wymondham
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
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)
Algorithm from machine learning
by: α k ( log α Θ + 1 ) + n Θ {\displaystyle \alpha k(\log _{\alpha }\Theta +1)+{\frac {n}{\Theta }}} . Nick Littlestone (1988). "Learning Quickly When
Winnow_(algorithm)
Regression analysis for modeling ordinal data
[yi = k].) The log-likelihood of the ordered logit model is analogous, using the logistic function instead of Φ. In machine learning, alternatives to
Ordinal_regression
Probabilistic logic programming language
samples of q {\displaystyle q} Learning from interpretations: learn the probabilities of ProbLog programs from data ProbLog can for example be used to calculate
ProbLog
Method of data analysis
RPCA to O ( max { m , n } r 2 log ( m ) log ( n ) log 1 ϵ ) {\displaystyle O\left(\max\{m,n\}r^{2}\log(m)\log(n)\log {\frac {1}{\epsilon }}\right)}
Robust principal component analysis
Robust_principal_component_analysis
Class of variational quantum states
}(S^{(i)}),} where O ( S ( i ) ) = ∂ log F ( S ( i ) ; W ) ∂ W k {\displaystyle O(S^{(i)})={\frac {\partial \log F(S^{(i)};W)}{\partial W_{k}}}} and
Neural_network_quantum_states
Probabilistic model
probability theory, statistics—particularly Bayesian statistics—and machine learning. Generally, probabilistic graphical models use a graph-based representation
Graphical_model
Smooth approximation of one-hot arg max
maximum function). The term "softmax" is also used for the closely related LogSumExp function, which is a smooth maximum. For this reason, some prefer the
Softmax_function
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
Historic building in Pennsylvania, US
The Log Cabin at the University of Pittsburgh, located near Forbes Avenue, in Pittsburgh, Pennsylvania adjacent to the school's Cathedral of Learning, serves
Log Cabin (University of Pittsburgh)
Log_Cabin_(University_of_Pittsburgh)
Subscription based education program for children 2–8
Early Learning Academy, is a digital education program targeted towards children ages 2–8, created by the educational technology company Age of Learning, Inc
ABCmouse
Building at the University of Pittsburgh
The Cathedral of Learning is a 42-story skyscraper that serves as the centerpiece of the University of Pittsburgh's (Pitt) main campus in the Oakland neighborhood
Cathedral_of_Learning
Concept in statistical science
take log pointwise predictive density: lppd ( y , Θ ) = ∑ i log 1 S ∑ s p ( y i ∣ Θ s ) {\displaystyle {\text{lppd}}(y,\Theta )=\sum _{i}\log {\frac
Widely applicable information criterion
Widely_applicable_information_criterion
Probability distribution
exponential family of distributions. Writing θ = log ( λ / ( 1 − λ ) ) {\displaystyle \theta =\log \left(\lambda /(1-\lambda )\right)} for the natural
Continuous Bernoulli distribution
Continuous_Bernoulli_distribution
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 which
Quantum_machine_learning
Optimization and sampling technique
{\displaystyle LR^{2}} being O ( log d ) {\displaystyle {\mathcal {O}}(\log d)} . Welling, Max; Teh, Yee Whye (2011). "Bayesian Learning via Stochastic Gradient
Stochastic gradient Langevin dynamics
Stochastic_gradient_Langevin_dynamics
Philosophy of teaching
are: audio and visual recordings samples of children's work photos learning logs display boards These approaches can help students develop pride in their
Emergent_curriculum
Decline of memory retention in time
approximate his forgetting curve: b = 100 k ( log ( t ) ) c + k {\displaystyle b={\frac {100k}{(\log(t))^{c}+k}}} Here, b {\displaystyle b} represents
Forgetting_curve
winter business". As winter arrives, Daffy begins to starve. He finds a log cabin owned by a starving fox and a weasel who want to eat him for dinner
Looney Tunes and Merrie Melodies filmography (1940–1949)
Looney_Tunes_and_Merrie_Melodies_filmography_(1940–1949)
Class of reinforcement learning algorithms
Policy gradient methods are a class of reinforcement learning algorithms and a sub-class of policy optimization methods. Unlike value-based methods which
Policy_gradient_method
Transforming data by taking the logarithm
In statistics, the log transformation is the application of the logarithmic function to each point in a data set—that is, each data point zi is replaced
Log transformation (statistics)
Log_transformation_(statistics)
First seminary serving Presbyterians in North America
The Log College, founded in 1727, was the first theological seminary serving Presbyterians in North America, and was located in what is now Warminster
Log_College
Smoothed ramp function
multivariable generalization of the logistic function. Both LogSumExp and softmax are used in machine learning. The convex conjugate (specifically, the Legendre
Softplus
Subfield of artificial intelligence
by the field of statistical relational learning; prominent examples of such combinations are LTNs and DeepProbLog. Since 2020, interest in neuro-symbolic
Neuro-symbolic_AI
Statistical model used in machine learning
{\displaystyle \log p_{K}(z_{K})=\log p_{0}(z_{0})-\sum _{i=1}^{K}\log \left|\det {\frac {df_{i}(z_{i-1})}{dz_{i-1}}}\right|} Learning probability distributions
Flow-based_generative_model
Academic discipline; examines how goal-driven social entities add and create knowledge
Christina Fang. The Muth model (1986) was the first to represent the learning curve in a log-linear form and focused on cost effectiveness in organization processes
Organizational_learning
Estimate of time taken for running an algorithm
multiplication, O ( n log n log log n ) {\displaystyle O(n\log n\log \log n)} In many cases, the O ( n log n ) {\displaystyle O(n\log n)} running time
Time_complexity
British painter (1803–1886)
design is attributed to Obadiah Short can be seen at Nicky Eastaugh's learning log for Textiles 1: Mixed Media for Textiles. Hoyte 2010, p. 39. "History
Obadiah_Short
Measure of dependence between two variables
1 2 log ( 2 π e σ i 2 ) = 1 2 + 1 2 log ( 2 π ) + log ( σ i ) , i ∈ { 1 , 2 } H ( X 1 , X 2 ) = 1 2 log [ ( 2 π e ) 2 | Σ | ] = 1 + log ( 2
Mutual_information
Type of computational algorithm
In computational complexity theory, a log-space reduction is a reduction computable by a deterministic Turing machine using logarithmic space. Conceptually
Log-space_reduction
Computer science data structure
O(n log n) storage and can be built in O(n log n) time. Segment trees support searching for all the intervals that contain a query point in time O(log n
Segment_tree
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
Programming paradigm
programming Probabilistic database Probabilistic programming ProbLog Statistical relational learning Riguzzi, Fabrizio; Swift, Theresa (2018-09-01), "A survey
Probabilistic logic programming
Probabilistic_logic_programming
Indian educationist and philanthropist
Nitish's leadership, S P Jain is credited with pioneering a multi-city learning model and developing a proprietorial software for the delivery of online
Nitish_Jain
Technique in natural language processing
as: g i = 1 + ∑ j p i j log p i j log n {\displaystyle g_{i}=1+\sum _{j}{\frac {p_{ij}\log p_{ij}}{\log n}}} a i j = g i log ( t f i j + 1 ) {\displaystyle
Latent_semantic_analysis
Mathematical model used for classification or regression
are a class of models frequently used for classification. In machine learning, it typically models the conditional distribution P(Y∣X), or it learns
Discriminative_model
Structured book club for young people
can take on many forms, ranging from writing, art, video/audiotapes, learning logs, student journals, personal responses etc. (Daniels, 1994). Extension
Literature_circle
Family of functions to transform data
normal log likelihood at its maximum to be written as follows: log ( L ( μ ^ , σ ^ ) ) = ( − n / 2 ) ( log ( 2 π σ ^ 2 ) + 1 ) + n ( λ − 1 ) log (
Power_transform
Scientific study of digital information
is 1/2 and the amount of information is expressed as − log 2 ( 1 / 2 ) {\displaystyle -\log _{2}(1/2)} = 1 bit of information. A key concept in information
Information_theory
Approximation for factorials
equivalent form log 2 n ! = n log 2 n − n log 2 e + O ( log 2 n ) . {\displaystyle \log _{2}n!=n\log _{2}n-n\log _{2}e+O(\log _{2}n).} The error
Stirling's_approximation
Way of inferring information from cross-covariance matrices
1875. CCA is now a cornerstone of multivariate statistics and multi-view learning, and a great number of interpretations and extensions have been proposed
Canonical_correlation
International nonprofit organization
children and adults in developing countries. buildOn views its service learning and school construction programs as a form of social activism that intends
BuildOn
Notion in statistics
the variance of the score: I ( θ ) = E [ ( ∂ ∂ θ log f ( X ; θ ) ) 2 | θ ] = ∫ R ( ∂ ∂ θ log f ( x ; θ ) ) 2 f ( x ; θ ) d x , {\displaystyle {\mathcal
Fisher_information
Topics referred to by the same term
railway station, Scotland; National Rail station code Log sequence number in a transaction log London News Network (also known as "London Sports Network/LSN")
LSN
Concept in probability and statistics
_{\theta }\log(l(\theta ))} where log ( l ( θ ) ) = log ( P ( x 1 | θ ) ) + log ( P ( x 2 | θ ) ) + log ( P ( x 3 | θ ) ) + . . . + log ( P ( x
Independent and identically distributed random variables
Independent_and_identically_distributed_random_variables
w , b , log μ , log ζ , M ) p ( w , b | log μ , log ζ , M ) p ( D | log μ , log ζ , M ) , {\displaystyle p(w,b|D,\log \mu ,\log \zeta ,\mathbb
Least-squares support vector machine
Least-squares_support_vector_machine
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