Read
AUTHORPÆDIA is hosted by Authorpædia Foundation, Inc. a U.S. non-profit organization.
Dai Sijie
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 34 subcategories, out of 34 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 (15 P)
- Machine learning researchers (181 P)
S
- Semisupervised learning (2 P)
- Supervised learning (6 P)
- Support vector machines (9 P)
U
Σ
- Machine learning stubs (128 P)
Pages in category "Machine learning"
The following 200 pages are in this category, out of approximately 252 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
- Alignment faking
- 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
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 and Knowledge Extraction
- 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
- Mechanistic interpretability
- Meta-Labeling
- 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
- Products and applications of OpenAI
- Programming by example
- Prompt engineering
- Proximal gradient methods for learning
- Purged cross-validation
- Pythia (machine learning)