Category:Machine learning
Machine learning is a branch of statistics and computer science which studies algorithms and architectures that learn from observed facts.
Subcategories
This category has the following 33 subcategories, out of 33 total.
A
B
- Bayesian networks (13 P)
- Blockmodeling (15 P)
C
D
E
- Ensemble learning (13 P)
G
- Genetic programming (11 P)
I
K
L
- Log-linear models (2 P)
- Loss functions (11 P)
M
R
- Reinforcement learning (13 P)
- Machine learning researchers (182 P)
S
- Semisupervised learning (2 P)
- Supervised learning (6 P)
- Support vector machines (9 P)
U
Pages in category "Machine learning"
The following 200 pages are in this category, out of approximately 247 total. This list may not reflect recent changes.
(previous page) (next page)A
- A Logical Calculus of the Ideas Immanent in Nervous Activity
- Accelerated Linear Algebra
- Action model learning
- Active learning (machine learning)
- Adversarial machine learning
- AI/ML Development Platform
- AIOps
- AIXI
- Algorithm selection
- Algorithmic bias
- Algorithmic inference
- Algorithmic party platforms in the United States
- 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
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
- Deep Tomographic Reconstruction
- Developmental robotics
- Digital signal processing and machine learning
- Discovery system (artificial intelligence)
- Document classification
- Domain adaptation
- Double descent
E
- Eager learning
- EfficientNet
- ELMo
- EM algorithm and GMM model
- Embedding (machine learning)
- 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
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
- Life-time of correlation
- Linear predictor function
- Linear separability
- Local case-control sampling
- Lottery ticket hypothesis
- 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 unlearning
- 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
- Mode collapse
- Model compression
- Mountain car problem
- Multi-armed bandit
- Multi-task learning
- Multimodal representation 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
- 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)