Category:Machine learning
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Machine learning is a branch of statistics and computer science which studies algorithms and architectures that learn from observed facts.
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Subcategories
This category has the following 35 subcategories, out of 35 total.
A
- Applied machine learning (2 C, 59 P)
- Artificial neural networks (3 C, 148 P)
B
- Bayesian networks (13 P)
- Blockmodeling (14 P)
C
- Classification algorithms (3 C, 86 P)
- Cluster analysis (2 C, 20 P)
- Computational learning theory (22 P)
D
- Data mining and machine learning software (3 C, 89 P)
- Datasets in machine learning (1 C, 11 P)
- Deep learning (3 C, 34 P)
- Dimension reduction (1 C, 46 P)
E
- Ensemble learning (13 P)
- Evolutionary algorithms (4 C, 50 P, 2 F)
G
- Genetic programming (14 P)
I
- Inductive logic programming (5 P)
K
- Kernel methods for machine learning (1 C, 17 P)
L
- Latent variable models (2 C, 26 P)
- Learning in computer vision (5 P)
- Log-linear models (2 P)
- Loss functions (10 P)
M
- Machine learning algorithms (1 C, 78 P)
- Machine learning task (1 C, 9 P)
- Markov models (2 C, 56 P)
O
R
- Reinforcement learning (11 P)
- Machine learning researchers (157 P)
S
- Semisupervised learning (2 P)
- Statistical natural language processing (1 C, 38 P)
- Structured prediction (1 C, 4 P)
- Supervised learning (5 P)
- Support vector machines (9 P)
U
- Unsupervised learning (2 C, 27 P)
Pages in category "Machine learning"
The following 200 pages are in this category, out of approximately 212 total. This list may not reflect recent changes.
(previous page) (next page)A
- Action model learning
- Active learning (machine learning)
- Adversarial machine learning
- AIXI
- Algorithm selection
- Algorithmic bias
- Algorithmic inference
- Anomaly detection
- Aporia (company)
- Apprenticeship learning
- Artificial intelligence in hiring
- Astrostatistics
- Attention (machine learning)
- Automated decision-making
- Automated machine learning
- Automation in construction
B
C
D
E
F
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)
- Machine Learning (journal)
- Machine learning control
- Machine learning in bioinformatics
- Machine learning in earth sciences
- Machine learning in physics
- Machine learning in video games
- Manifold hypothesis
- Manifold regularization
- The Master Algorithm
- Matchbox Educable Noughts and Crosses Engine
- Matrix regularization
- Maximum inner-product search
- Meta-learning (computer science)
- MLOps
- 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
P
- Paraphrasing (computational linguistics)
- Parity learning
- Pattern language (formal languages)
- Pattern recognition
- Perceiver
- 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)
R
S
- Sample complexity
- Self-supervised learning
- Semantic analysis (machine learning)
- Semantic folding
- Semi-supervised learning
- Sequence labeling
- Similarity learning
- Lynda Soderholm
- Solomonoff's theory of inductive inference
- Spatial embedding
- Spike-and-slab regression
- Stability (learning theory)
- Statistical learning theory
- Statistical relational learning
- Stochastic block model
- Structural risk minimization
- Structured sparsity regularization
- Surrogate model
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