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Subcategories
This category has the following 25 subcategories, out of 25 total.
A
- Applied machine learning (80 P)
B
- Bayesian networks (14 P)
C
- Computational learning theory (23 P)
D
- Datasets in machine learning (16 P)
E
G
- Genetic programming (15 P)
I
- Inductive logic programming (10 P)
K
L
- Log-linear models (2 P)
- Loss functions (11 P)
M
R
- Reinforcement learning (15 P)
- Machine learning researchers (168 P)
S
- Support vector machines (13 P)
U
- Unsupervised learning (35 P)
Pages in category "Machine learning"
The following 200 pages are in this category, out of 262 total.
(previous page) (next page)A
- Software:Accelerated Linear Algebra
- Action model learning
- Active learning (machine learning)
- Adversarial machine learning
- Social:AI Constitution
- AIVA
- AIXI
- Algorithm selection
- Algorithmic bias
- Algorithmic inference
- Anomaly detection
- Company:Aporia
- Company:Appen
- Apprenticeship learning
- Artificial intelligence in hiring
- Associative classifier
- Astronomy: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
- Software:BigDL
- Binary classification
- Bing Predicts
- Company:Bioserenity
- Blockmodeling
- Bradley–Terry model
- Company:BrainChip
C
- Cancer Likelihood in Plasma
- Category utility
- Organization:Center for Biological and Computational Learning
- CIML community portal
- Software:Claude (language model)
- Cleverbot
- Cognitive robotics
- Concept drift
- Concept learning
- Conditional random field
- Confusion matrix
- Cost-sensitive machine learning
- Coupled pattern learner
- Cross-entropy method
- Cross-validation (statistics)
- Curse of dimensionality
D
E
- Eager learning
- ELMo
- EM algorithm and GMM model
- Empirical dynamic modeling
- Empirical risk minimization
- Physics:Energy based model
- Physics: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)
- Fine-tuning (machine learning)
- Flow-based generative model
- Software:Flux (machine-learning framework)
- Force control
- Formal concept analysis
- Organization:Foundation models
G
H
I
K
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
- Leave-one-out error
- Life-time of correlation
- Linear predictor function
- Linear separability
- Local case-control sampling
- Lyra (codec)
M
- M-Theory (learning framework)
- M-theory (learning framework)
- Machine learning control
- Machine learning in bioinformatics
- Machine learning in earth sciences
- Physics:Machine learning in physics
- Software:Machine learning in video games
- Machine learning potential
- Manifold hypothesis
- Manifold regularization
- The Master Algorithm
- Matchbox Educable Noughts and Crosses Engine
- Matrix regularization
- Biology:Matthews correlation coefficient
- Maximum inner-product search
- Meta learning (computer science)
- Meta-learning (computer science)
- MLOps
- Engineering:Mountain car problem
- Multi-agent learning
- 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
- Multivariate adaptive regression splines
N
P
- Paraphrasing (computational linguistics)
- Parity learning
- Pattern language (formal languages)
- Pattern recognition
- Perceiver
- Biology: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
- Social:Prompt engineering
- Proximal gradient methods for learning
- Pythia (machine learning)
