Outline of 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 within computer science that evolved from the study of pattern recognition and computational learning theory. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". ML involves the study and construction of algorithms that can learn from and make predictions on data. These algorithms operate by building a model from a training set of example observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

How can machine learning be categorized?

An academic discipline

A branch of science

An applied science

A subfield of computer science

A branch of artificial intelligence

A subfield of soft computing

Application of statistics

Paradigms of machine learning

Supervised learning, where the model is trained on labeled data

Unsupervised learning, where the model tries to identify patterns in unlabeled data

Reinforcement learning, where the model learns to make decisions by receiving rewards or penalties.

Applications of machine learning

Applications of machine learning

Bioinformatics

Biomedical informatics

Computer vision

Customer relationship management

Data mining

Earth sciences

Email filtering

Inverted pendulum (balance and equilibrium system)

Natural language processing

Named Entity Recognition

Automatic summarization

Automatic taxonomy construction

Dialog system

Grammar checker

Language recognition

Handwriting recognition

Optical character recognition

Speech recognition

Text to Speech Synthesis

Speech Emotion Recognition

Machine translation

Question answering

Speech synthesis

Text mining

Term frequency–inverse document frequency

Text simplification

Pattern recognition

Facial recognition system

Handwriting recognition

Image recognition

Optical character recognition

Speech recognition

Recommendation system

Collaborative filtering

Content-based filtering

Hybrid recommender systems

Search engine

Search engine optimization

Social engineering

Machine learning hardware

Graphics processing unit

Tensor processing unit

Vision processing unit

Machine learning tools

Comparison of machine learning software

Comparison of deep learning software

Machine learning frameworks

Proprietary machine learning frameworks

Amazon Machine Learning

Microsoft Azure Machine Learning Studio

DistBelief (replaced by TensorFlow)

Open source machine learning frameworks

Apache Singa

Apache MXNet

Caffe

PyTorch

mlpack

TensorFlow

Torch

CNTK

Accord.Net

Jax

MLJ.jl – A machine learning framework for Julia

Machine learning libraries

Deeplearning4j

Theano

scikit-learn

Keras

Machine learning algorithms

Machine learning methods

Instance-based algorithm

K-nearest neighbors algorithm (KNN)

Learning vector quantization (LVQ)

Self-organizing map (SOM)

Regression analysis

Logistic regression

Ordinary least squares regression (OLSR)

Linear regression

Stepwise regression

Multivariate adaptive regression splines (MARS)

Regularization algorithm

Ridge regression

Least Absolute Shrinkage and Selection Operator (LASSO)

Elastic net

Least-angle regression (LARS)

Classifiers

Probabilistic classifier

Naive Bayes classifier

Binary classifier

Linear classifier

Hierarchical classifier

Dimensionality reduction

Dimensionality reduction

Canonical correlation analysis (CCA)

Factor analysis

Feature extraction

Feature selection

Independent component analysis (ICA)

Linear discriminant analysis (LDA)

Multidimensional scaling (MDS)

Non-negative matrix factorization (NMF)

Partial least squares regression (PLSR)

Principal component analysis (PCA)

Principal component regression (PCR)

Projection pursuit

Sammon mapping

t-distributed stochastic neighbor embedding (t-SNE)

Ensemble learning

Ensemble learning

AdaBoost

Boosting

Bootstrap aggregating (also "bagging" or "bootstrapping")

Ensemble averaging

Gradient boosted decision tree (GBDT)

Gradient boosting

Random Forest

Stacked Generalization

Meta-learning

Meta-learning

Inductive bias

Metadata

Reinforcement learning

Reinforcement learning

Q-learning

State–action–reward–state–action (SARSA)

Temporal difference learning (TD)

Learning Automata

Supervised learning

Supervised learning

Averaged one-dependence estimators (AODE)

Artificial neural network

Case-based reasoning

Gaussian process regression

Gene expression programming

Group method of data handling (GMDH)

Inductive logic programming

Instance-based learning

Lazy learning

Learning Automata

Learning Vector Quantization

Logistic Model Tree

Minimum message length (decision trees, decision graphs, etc.)

Nearest Neighbor Algorithm

Analogical modeling

Probably approximately correct learning (PAC) learning

Ripple down rules, a knowledge acquisition methodology

Symbolic machine learning algorithms

Support vector machines

Random Forests

Ensembles of classifiers

Bootstrap aggregating (bagging)

Boosting (meta-algorithm)

Ordinal classification

Conditional Random Field

ANOVA

Quadratic classifiers

k-nearest neighbor

Boosting

SPRINT

Bayesian networks

Naive Bayes

Hidden Markov models

Hierarchical hidden Markov model

Bayesian

Bayesian statistics

Bayesian knowledge base

Naive Bayes

Gaussian Naive Bayes

Multinomial Naive Bayes

Averaged One-Dependence Estimators (AODE)

Bayesian Belief Network (BBN)

Bayesian Network (BN)

Decision tree algorithms

Decision tree algorithm

Decision tree

Classification and regression tree (CART)

Iterative Dichotomiser 3 (ID3)

C4.5 algorithm

C5.0 algorithm

Chi-squared Automatic Interaction Detection (CHAID)

Decision stump

Conditional decision tree

ID3 algorithm

Random forest

SLIQ

Linear classifier

Linear classifier

Fisher's linear discriminant

Linear regression

Logistic regression

Multinomial logistic regression

Naive Bayes classifier

Perceptron

Support vector machine

Unsupervised learning

Unsupervised learning

Expectation-maximization algorithm

Vector Quantization

Generative topographic map

Information bottleneck method

Association rule learning algorithms

Apriori algorithm

Eclat algorithm

Artificial neural networks

Artificial neural network

Feedforward neural network

Extreme learning machine

Convolutional neural network

Recurrent neural network

Long short-term memory (LSTM)

Logic learning machine

Self-organizing map

Association rule learning

Association rule learning

Apriori algorithm

Eclat algorithm

FP-growth algorithm

Hierarchical clustering

Hierarchical clustering

Single-linkage clustering

Conceptual clustering

Cluster analysis

Cluster analysis

BIRCH

DBSCAN

Expectation–maximization (EM)

Fuzzy clustering

Hierarchical clustering

k-means clustering

k-medians

Mean-shift

OPTICS algorithm

Anomaly detection

Anomaly detection

k-nearest neighbors algorithm (k-NN)

Local outlier factor

Semi-supervised learning

Semi-supervised learning

Active learning

Generative models

Low-density separation

Graph-based methods

Co-training

Transduction

Deep learning

Deep learning

Deep belief networks

Deep Boltzmann machines

Deep Convolutional neural networks

Deep Recurrent neural networks

Hierarchical temporal memory

Generative Adversarial Network

Style transfer

Transformer

Stacked Auto-Encoders

Other machine learning methods and problems

Anomaly detection

Association rules

Bias-variance dilemma

Classification

Multi-label classification

Clustering

Data Pre-processing

Empirical risk minimization

Feature engineering

Feature learning

Learning to rank

Occam learning

Online machine learning

PAC learning

Regression

Reinforcement Learning

Semi-supervised learning

Statistical learning

Structured prediction

Graphical models

Bayesian network

Conditional random field (CRF)

Hidden Markov model (HMM)

Unsupervised learning

VC theory

Machine learning research

List of artificial intelligence projects

List of datasets for machine learning research

History of machine learning

History of machine learning

Timeline of machine learning

Machine learning projects

Machine learning projects:

DeepMind

Google Brain

OpenAI

Meta AI

Hugging Face

Machine learning organizations

Machine learning conferences and workshops

Artificial Intelligence and Security (AISec) (co-located workshop with CCS)

Conference on Neural Information Processing Systems (NIPS)

ECML PKDD

International Conference on Machine Learning (ICML)

ML4ALL (Machine Learning For All)

Machine learning publications

Books on machine learning

Mathematics for Machine Learning

Hands-On Machine Learning Scikit-Learn, Keras, and TensorFlow

The Hundred-Page Machine Learning Book

Machine learning journals

Machine Learning

Journal of Machine Learning Research (JMLR)

Neural Computation

Persons influential in machine learning

Alberto Broggi

Andrei Knyazev

Andrew McCallum

Andrew Ng

Anuraag Jain

Armin B. Cremers

Ayanna Howard

Barney Pell

Ben Goertzel

Ben Taskar

Bernhard Schölkopf

Brian D. Ripley

Christopher G. Atkeson

Corinna Cortes

Demis Hassabis

Douglas Lenat

Eric Xing

Ernst Dickmanns

Geoffrey Hinton

Hans-Peter Kriegel

Hartmut Neven

Heikki Mannila

Ian Goodfellow

Jacek M. Zurada

Jaime Carbonell

Jeremy Slovak

Jerome H. Friedman

John D. Lafferty

John Platt

Julie Beth Lovins

Jürgen Schmidhuber

Karl Steinbuch

Katia Sycara

Leo Breiman

Lise Getoor

Luca Maria Gambardella

Léon Bottou

Marcus Hutter

Mehryar Mohri

Michael Collins

Michael I. Jordan

Michael L. Littman

Nando de Freitas

Ofer Dekel

Oren Etzioni

Pedro Domingos

Peter Flach

Pierre Baldi

Pushmeet Kohli

Ray Kurzweil

Rayid Ghani

Ross Quinlan

Salvatore J. Stolfo

Sebastian Thrun

Selmer Bringsjord

Sepp Hochreiter

Shane Legg

Stephen Muggleton

Steve Omohundro

Tom M. Mitchell

Trevor Hastie

Vasant Honavar

Vladimir Vapnik

Yann LeCun

Yasuo Matsuyama

Yoshua Bengio

Zoubin Ghahramani

See also

Outline of artificial intelligence

Outline of computer vision

Outline of deep learning

Outline of robotics

Accuracy paradox

Action model learning

Activation function

Activity recognition

ADALINE

Adaptive neuro fuzzy inference system

Adaptive resonance theory

Additive smoothing

Adjusted mutual information

AIVA

AIXI

AlchemyAPI

AlexNet

Algorithm selection

Algorithmic inference

Algorithmic learning theory

AlphaGo

AlphaGo Zero

Alternating decision tree

Apprenticeship learning

Causal Markov condition

Competitive learning

Concept learning

Decision tree learning

Differentiable programming

Distribution learning theory

Eager learning

End-to-end reinforcement learning

Error tolerance (PAC learning)

Explanation-based learning

Feature

GloVe

Hyperparameter

Inferential theory of learning

Learning automata

Learning classifier system

Learning rule

Learning with errors

M-Theory (learning framework)

Machine learning control

Machine learning in bioinformatics

Margin

Markov chain geostatistics

Markov chain Monte Carlo (MCMC)

Markov information source

Markov logic network

Markov model

Markov random field

Markovian discrimination

Maximum-entropy Markov model

Multi-armed bandit

Multi-task learning

Multilinear subspace learning

Multimodal learning

Multiple instance learning

Multiple-instance learning

Never-Ending Language Learning

Offline learning

Parity learning

Population-based incremental learning

Predictive learning

Preference learning

Proactive learning

Proximal gradient methods for learning

Semantic analysis

Similarity learning

Sparse dictionary learning

Stability (learning theory)

Statistical learning theory

Statistical relational learning

Tanagra

Transfer learning

Variable-order Markov model

Version space learning

Waffles

Weka

Loss function

Loss functions for classification

Mean squared error (MSE)

Mean squared prediction error (MSPE)

Taguchi loss function

Low-energy adaptive clustering hierarchy

Other

Anne O'Tate

Ant colony optimization algorithms

Anthony Levandowski

Anti-unification (computer science)

Apache Flume

Apache Giraph

Apache Mahout

Apache SINGA

Apache Spark

Apache SystemML

Aphelion (software)

Arabic Speech Corpus

Archetypal analysis

Arthur Zimek

Artificial ants

Artificial bee colony algorithm

Artificial development

Artificial immune system

Astrostatistics

Averaged one-dependence estimators

Bag-of-words model

Balanced clustering

Ball tree

Base rate

Bat algorithm

Baum–Welch algorithm

Bayesian hierarchical modeling

Bayesian interpretation of kernel regularization

Bayesian optimization

Bayesian structural time series

Bees algorithm

Behavioral clustering

Bernoulli scheme

Bias–variance tradeoff

Biclustering

BigML

Binary classification

Bing Predicts

Bio-inspired computing

Biogeography-based optimization

Biplot

Bondy's theorem

Bongard problem

Bradley–Terry model

BrownBoost

Brown clustering

Burst error

CBCL (MIT)

CIML community portal

CMA-ES

CURE data clustering algorithm

Cache language model

Calibration (statistics)

Canonical correspondence analysis

Canopy clustering algorithm

Cascading classifiers

Category utility

CellCognition

Cellular evolutionary algorithm

Chi-square automatic interaction detection

Chromosome (genetic algorithm)

Classifier chains

Cleverbot

Clonal selection algorithm

Cluster-weighted modeling

Clustering high-dimensional data

Clustering illusion

CoBoosting

Cobweb (clustering)

Cognitive computer

Cognitive robotics

Collostructional analysis

Common-method variance

Complete-linkage clustering

Computer-automated design

Concept class

Concept drift

Conference on Artificial General Intelligence

Conference on Knowledge Discovery and Data Mining

Confirmatory factor analysis

Confusion matrix

Congruence coefficient

Connect (computer system)

Consensus clustering

Constrained clustering

Constrained conditional model

Constructive cooperative coevolution

Correlation clustering

Correspondence analysis

Coupled pattern learner

Cross-entropy method

Cross-validation (statistics)

Crossover (genetic algorithm)

Cuckoo search

Cultural algorithm

Cultural consensus theory

Curse of dimensionality

DADiSP

DARPA LAGR Program

Darkforest

Dartmouth workshop

DarwinTunes

Data Mining Extensions

Data exploration

Data pre-processing

Data stream clustering

Dataiku

Davies–Bouldin index

Decision boundary

Decision list

Decision tree model

Deductive classifier

DeepArt

DeepDream

Deep Web Technologies

Defining length

Dendrogram

Dependability state model

Detailed balance

Determining the number of clusters in a data set

Detrended correspondence analysis

Developmental robotics

Diffbot

Differential evolution

Discrete phase-type distribution

Discriminative model

Dissociated press

Distributed R

Dlib

Document classification

Documenting Hate

Domain adaptation

Doubly stochastic model

Dual-phase evolution

Dunn index

Dynamic Bayesian network

Dynamic Markov compression

Dynamic topic model

Dynamic unobserved effects model

EDLUT

ELKI

Edge recombination operator

Effective fitness

Elastic map

Elastic matching

Elbow method (clustering)

Emergent (software)

Encog

Entropy rate

Erkki Oja

Eurisko

European Conference on Artificial Intelligence

Evaluation of binary classifiers

Evolution strategy

Evolution window

Evolutionary Algorithm for Landmark Detection

Evolutionary algorithm

Evolutionary art

Evolutionary music

Evolutionary programming

Evolvability (computer science)

Evolved antenna

Evolver (software)

Evolving classification function

Expectation propagation

Exploratory factor analysis

F1 score

FLAME clustering

Factor analysis of mixed data

Factor graph

Factor regression model

Factored language model

Farthest-first traversal

Fast-and-frugal trees

Feature Selection Toolbox

Feature hashing

Feature scaling

Feature vector

Firefly algorithm

First-difference estimator

First-order inductive learner

Fish School Search

Fisher kernel

Fitness approximation

Fitness function

Fitness proportionate selection

Fluentd

Folding@home

Formal concept analysis

Forward algorithm

Fowlkes–Mallows index

Frederick Jelinek

Frrole

Functional principal component analysis

GATTO

GLIMMER

Gary Bryce Fogel

Gaussian adaptation

Gaussian process

Gaussian process emulator

Gene prediction

General Architecture for Text Engineering

Generalization error

Generalized canonical correlation

Generalized filtering

Generalized iterative scaling

Generalized multidimensional scaling

Generative adversarial network

Generative model

Genetic algorithm

Genetic algorithm scheduling

Genetic algorithms in economics

Genetic fuzzy systems

Genetic memory (computer science)

Genetic operator

Genetic programming

Genetic representation

Geographical cluster

Gesture Description Language

Geworkbench

Glossary of artificial intelligence

Glottochronology

Golem (ILP)

Google matrix

Grafting (decision trees)

Gramian matrix

Grammatical evolution

Granular computing

GraphLab

Graph kernel

Gremlin (programming language)

Growth function

HUMANT (HUManoid ANT) algorithm

Hammersley–Clifford theorem

Harmony search

Hebbian theory

Hidden Markov random field

Hidden semi-Markov model

Hierarchical hidden Markov model

Higher-order factor analysis

Highway network

Hinge loss

Holland's schema theorem

Hopkins statistic

Hoshen–Kopelman algorithm

Huber loss

IRCF360

Ian Goodfellow

Ilastik

Ilya Sutskever

Immunocomputing

Imperialist competitive algorithm

Inauthentic text

Incremental decision tree

Induction of regular languages

Inductive bias

Inductive probability

Inductive programming

Influence diagram

Information Harvesting

Information gain in decision trees

Information gain ratio

Inheritance (genetic algorithm)

Instance selection

Intel RealSense

Interacting particle system

Interactive machine translation

International Joint Conference on Artificial Intelligence

International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

International Semantic Web Conference

Iris flower data set

Island algorithm

Isotropic position

Item response theory

Iterative Viterbi decoding

JOONE

Jabberwacky

Jaccard index

Jackknife variance estimates for random forest

Java Grammatical Evolution

Joseph Nechvatal

Jubatus

Julia (programming language)

Junction tree algorithm

k-SVD

k-means++

k-medians clustering

k-medoids

KNIME

KXEN Inc.

k q-flats

Kaggle

Kalman filter

Katz's back-off model

Kernel adaptive filter

Kernel density estimation

Kernel eigenvoice

Kernel embedding of distributions

Kernel method

Kernel perceptron

Kernel random forest

Kinect

Klaus-Robert Müller

Kneser–Ney smoothing

Knowledge Vault

Knowledge integration

LIBSVM

LPBoost

Labeled data

LanguageWare

Language identification in the limit

Language model

Large margin nearest neighbor

Latent Dirichlet allocation

Latent class model

Latent semantic analysis

Latent variable

Latent variable model

Lattice Miner

Layered hidden Markov model

Learnable function class

Least squares support vector machine

Leslie P. Kaelbling

Linear genetic programming

Linear predictor function

Linear separability

Linkurious

Lior Ron (business executive)

List of genetic algorithm applications

List of metaphor-based metaheuristics

List of text mining software

Local case-control sampling

Local independence

Local tangent space alignment

Locality-sensitive hashing

Log-linear model

Logistic model tree

Low-rank approximation

Low-rank matrix approximations

MATLAB

MIMIC (immunology)

MXNet

Mallet (software project)

Manifold regularization

Margin-infused relaxed algorithm

Margin classifier

Mark V. Shaney

Massive Online Analysis

Matrix regularization

Matthews correlation coefficient

Mean shift

Mean squared error

Mean squared prediction error

Measurement invariance

Medoid

MeeMix

Melomics

Memetic algorithm

Meta-optimization

Mexican International Conference on Artificial Intelligence

Michael Kearns (computer scientist)

MinHash

Mixture model

Mlpy

Models of DNA evolution

Moral graph

Mountain car problem

Movidius

Multi-armed bandit

Multi-label classification

Multi expression programming

Multiclass classification

Multidimensional analysis

Multifactor dimensionality reduction

Multilinear principal component analysis

Multiple correspondence analysis

Multiple discriminant analysis

Multiple factor analysis

Multiple sequence alignment

Multiplicative weight update method

Multispectral pattern recognition

Mutation (genetic algorithm)

N-gram

NOMINATE (scaling method)

Native-language identification

Natural Language Toolkit

Natural evolution strategy

Nearest-neighbor chain algorithm

Nearest centroid classifier

Nearest neighbor search

Neighbor joining

Nest Labs

NetMiner

NetOwl

Neural Designer

Neural Engineering Object

Neural modeling fields

Neural network software

NeuroSolutions

Neuroevolution

Neuroph

Niki.ai

Noisy channel model

Noisy text analytics

Nonlinear dimensionality reduction

Novelty detection

Nuisance variable

One-class classification

Onnx

OpenNLP

Optimal discriminant analysis

Oracle Data Mining

Orange (software)

Ordination (statistics)

Overfitting

PROGOL

PSIPRED

Pachinko allocation

PageRank

Parallel metaheuristic

Parity benchmark

Part-of-speech tagging

Particle swarm optimization

Path dependence

Pattern language (formal languages)

Peltarion Synapse

Perplexity

Persian Speech Corpus

Pietro Perona

Pipeline Pilot

Piranha (software)

Pitman–Yor process

Plate notation

Polynomial kernel

Pop music automation

Population process

Portable Format for Analytics

Predictive Model Markup Language

Predictive state representation

Preference regression

Premature convergence

Principal geodesic analysis

Prior knowledge for pattern recognition

Prisma (app)

Probabilistic Action Cores

Probabilistic context-free grammar

Probabilistic latent semantic analysis

Probabilistic soft logic

Probability matching

Probit model

Product of experts

Programming with Big Data in R

Proper generalized decomposition

Pruning (decision trees)

Pushpak Bhattacharyya

Q methodology

Qloo

Quality control and genetic algorithms

Quantum Artificial Intelligence Lab

Queueing theory

Quick, Draw!

R (programming language)

Rada Mihalcea

Rademacher complexity

Radial basis function kernel

Rand index

Random indexing

Random projection

Random subspace method

Ranking SVM

RapidMiner

Rattle GUI

Raymond Cattell

Reasoning system

Regularization perspectives on support vector machines

Relational data mining

Relationship square

Relevance vector machine

Relief (feature selection)

Renjin

Repertory grid

Representer theorem

Reward-based selection

Richard Zemel

Right to explanation

RoboEarth

Robust principal component analysis

RuleML Symposium

Rule induction

Rules extraction system family

SAS (software)

SNNS

SPSS Modeler

SUBCLU

Sample complexity

Sample exclusion dimension

Santa Fe Trail problem

Savi Technology

Schema (genetic algorithms)

Search-based software engineering

Selection (genetic algorithm)

Self-Service Semantic Suite

Semantic folding

Semantic mapping (statistics)

Semidefinite embedding

Sense Networks

Sensorium Project

Sequence labeling

Sequential minimal optimization

Shattered set

Shogun (toolbox)

Silhouette (clustering)

SimHash

SimRank

Similarity measure

Simple matching coefficient

Simultaneous localization and mapping

Sinkov statistic

Sliced inverse regression

Snakes and Ladders

Soft independent modelling of class analogies

Soft output Viterbi algorithm

Solomonoff's theory of inductive inference

SolveIT Software

Spectral clustering

Spike-and-slab variable selection

Statistical machine translation

Statistical parsing

Statistical semantics

Stefano Soatto

Stephen Wolfram

Stochastic block model

Stochastic cellular automaton

Stochastic diffusion search

Stochastic grammar

Stochastic matrix

Stochastic universal sampling

Stress majorization

String kernel

Structural equation modeling

Structural risk minimization

Structured sparsity regularization

Structured support vector machine

Subclass reachability

Sufficient dimension reduction

Sukhotin's algorithm

Sum of absolute differences

Sum of absolute transformed differences

Swarm intelligence

Switching Kalman filter

Symbolic regression

Synchronous context-free grammar

Syntactic pattern recognition

TD-Gammon

TIMIT

Teaching dimension

Teuvo Kohonen

Textual case-based reasoning

Theory of conjoint measurement

Thomas G. Dietterich

Thurstonian model

Topic model

Tournament selection

Training, test, and validation sets

Transiogram

Trax Image Recognition

Trigram tagger

Truncation selection

Tucker decomposition

UIMA

UPGMA

Ugly duckling theorem

Uncertain data

Uniform convergence in probability

Unique negative dimension

Universal portfolio algorithm

User behavior analytics

VC dimension

VIGRA

Validation set

Vapnik–Chervonenkis theory

Variable-order Bayesian network

Variable kernel density estimation

Variable rules analysis

Variational message passing

Varimax rotation

Vector quantization

Vicarious (company)

Viterbi algorithm

Vowpal Wabbit

WACA clustering algorithm

WPGMA

Ward's method

Weasel program

Whitening transformation

Winnow (algorithm)

Win–stay, lose–switch

Witness set

Wolfram Language

Wolfram Mathematica

Writer invariant

Xgboost

Yooreeka

Zeroth (software)

Further reading

Trevor Hastie, Robert Tibshirani and Jerome H. Friedman (2001). The Elements of Statistical Learning, Springer.