Category:Machine learning

A

• Applied machine learning (3 C, 59 P)
• Artificial neural networks (4 C, 154 P)

B

• Bayesian networks (13 P)
• Blockmodeling (15 P)

C

• Classification algorithms (3 C, 85 P)
• Cluster analysis (2 C, 22 P)
• Computational learning theory (22 P)
• Artificial intelligence conferences (1 C, 32 P)
• Signal processing conferences (6 P)

D

• Data mining and machine learning software (3 C, 89 P)
• Datasets in machine learning (1 C, 12 P)
• Dimension reduction (1 C, 46 P)

E

• Ensemble learning (13 P)
• Evolutionary algorithms (4 C, 50 P)

G

• Genetic programming (14 P)

I

• Inductive logic programming (7 P)

K

• Kernel methods for machine learning (1 C, 18 P)

L

• Latent variable models (2 C, 26 P)
• Learning in computer vision (5 P)
• Log-linear models (2 P)
• Loss functions (11 P)

M

• Machine learning algorithms (1 C, 84 P)
• Machine learning task (2 C, 9 P)
• Markov models (2 C, 62 P)

O

• Ontology learning (computer science) (2 P)

R

• Reinforcement learning (13 P)
• Machine learning researchers (168 P)
• Natural language processing researchers (133 P)

S

• Semisupervised learning (2 P)
• Statistical natural language processing (1 C, 38 P)
• Structured prediction (1 C, 4 P)
• Supervised learning (6 P)
• Support vector machines (9 P)

U

• Unsupervised learning (2 C, 26 P)

• Bayesian learning mechanisms
• Machine learning

*

• Outline of machine learning

0–9

• 80 Million Tiny Images

A

• A logical calculus of the ideas immanent in nervous activity
• Accelerated Linear Algebra
• Action model learning
• Active learning (machine learning)
• Adversarial machine learning
• AIOps
• AIXI
• Algorithm selection
• Algorithmic bias
• Algorithmic inference
• Anomaly detection
• Aporia (company)
• Apprenticeship learning
• Artificial intelligence in hiring
• Astrostatistics
• Attention (machine learning)
• Audio inpainting
• Automated decision-making
• Automated machine learning
• Automation in construction

B

• Bag-of-words model
• Ball tree
• Base rate
• Bayesian interpretation of kernel regularization
• Bayesian optimization
• Bayesian regret
• Bayesian structural time series
• Bias–variance tradeoff
• Binary classification
• Bioserenity
• Bradley–Terry model

C

• Category utility
• CIML community portal
• Claude (language model)
• Cognitive robotics
• Concept drift
• Conditional random field
• Confusion matrix
• Contrastive Language-Image Pre-training
• Cost-sensitive machine learning
• Coupled pattern learner
• Cross-entropy method
• Cross-validation (statistics)
• Curse of dimensionality

D

• Data augmentation
• Data exploration
• Data preprocessing
• Data-driven astronomy
• Data-driven model
• Decision list
• Decision tree pruning
• Developmental robotics
• Discovery system (AI research)
• Document classification
• Domain adaptation
• Double descent

E

• Eager learning
• EfficientNet
• ELMo
• EM algorithm and GMM model
• Empirical dynamic modeling
• Empirical risk minimization
• Energy-based model
• Equalized odds
• Evaluation of binary classifiers
• Evolvability (computer science)
• Expectation propagation
• Explanation-based learning
• Exploration-exploitation dilemma

F

• Fairness (machine learning)
• Feature (machine learning)
• Feature engineering
• Feature hashing
• Feature learning
• Feature scaling
• Federated learning
• Fine-tuning (deep learning)
• Flow-based generative model
• Flux (machine-learning framework)
• Force control
• Formal concept analysis

G

• Generative artificial intelligence
• Generative model
• Geometric feature learning
• Glossary of artificial intelligence
• Google JAX
• Granular computing
• Grokking (machine learning)

H

• Hallucination (artificial intelligence)
• Hidden layer
• Hierarchical navigable small world
• Highway network
• Hugging Face
• Human-in-the-loop
• Hyperparameter (machine learning)
• Hyperparameter optimization

I

• In-context learning (natural language processing)
• Inauthentic text
• Inception score
• Inductive bias
• Inductive probability
• Inductive programming
• Inferential theory of learning
• Instance selection
• Instance-based learning
• Intelligent automation
• Isotropic position

J

• Journal of Machine Learning Research

K

• Kernel density estimation
• Kernel embedding of distributions
• Knowledge graph embedding
• Knowledge integration

L

• Labeled data
• Lazy learning
• Leakage (machine learning)
• Learnable function class
• Learning automaton
• Learning curve (machine learning)
• Learning rate
• Learning to rank
• Learning with errors
• Life-time of correlation
• Linear predictor function
• Linear separability
• Local case-control sampling
• Lyra (codec)

M

• M-theory (learning framework)
• Machine Learning (journal)
• Machine learning control
• Machine learning in bioinformatics
• Machine learning in earth sciences
• Machine learning in physics
• Machine learning in video games
• Machine-learned interatomic potential
• Manifold hypothesis
• Manifold regularization
• The Master Algorithm
• Matchbox Educable Noughts and Crosses Engine
• Matrix regularization
• Maximum inner-product search
• Meta-learning (computer science)
• MLOps
• MobileNet
• Model compression
• Mountain car problem
• Multi-armed bandit
• Multi-task learning
• Multimodal sentiment analysis
• Multiple instance learning
• Multiple-instance learning
• Multiplicative weight update method
• Multitask optimization
• Multivariate adaptive regression spline

N

• Native-language identification
• Nature Machine Intelligence
• Neural modeling fields
• Neural network quantum states
• Normalization (machine learning)
• Novelty detection

O

• Offline learning
• Overfitting

P

• Paraphrasing (computational linguistics)
• Parity learning
• Pattern language (formal languages)
• Pattern recognition
• Perceiver
• PHerc. Paris. 4
• Phi coefficient
• Predictive learning
• Predictive state representation
• Preference learning
• Prior knowledge for pattern recognition
• Proactive learning
• Proaftn
• Probabilistic numerics
• Probability matching
• Product of experts
• Programming by example
• Prompt engineering
• Proximal gradient methods for learning
• Pythia (machine learning)

Q

• Quantification (machine learning)
• Quantum machine learning

R

• Rabbit r1
• Rademacher complexity
• Random feature
• Reciprocal human machine learning
• Relational data mining
• Reparameterization trick
• Right to explanation
• Robot learning
• Robotic process automation
• ROCm
• Rule induction

S

• Sample complexity
• Self-supervised learning