User:Mathurin.ache/Books/Machine Learningv2
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Machine Learning
- Introduction and Main Principles
- Machine learning
- Data analysis
- Occam's razor
- Curse of dimensionality
- No free lunch theorem
- Accuracy paradox
- Overfitting
- Regularization (machine learning)
- Inductive bias
- Data dredging
- Ugly duckling theorem
- Uncertain data
- Background and Preliminaries
- Knowledge discovery in Databases
- Knowledge discovery
- Data mining
- Predictive analytics
- Predictive modelling
- Business intelligence
- Reactive business intelligence
- Business analytics
- Reactive business intelligence
- Pattern recognition
- Statistics
- Exploratory data analysis
- Covariate
- Statistical inference
- Algorithmic inference
- Bayesian inference
- Base rate
- Bias (statistics)
- Gibbs sampling
- Cross-entropy method
- Latent variable
- Maximum likelihood
- Maximum a posteriori estimation
- Expectation–maximization algorithm
- Expectation propagation
- Kullback–Leibler divergence
- Generative model
- Main Learning Paradigms
- Supervised learning
- Unsupervised learning
- Active learning (machine learning)
- Reinforcement learning
- Multi-task learning
- Transduction
- Explanation-based learning
- Offline learning
- Online learning model
- Online machine learning
- Hyperparameter optimization
- Classification Tasks
- Classification in machine learning
- Concept class
- Features (pattern recognition)
- Feature vector
- Feature space
- Concept learning
- Binary classification
- Decision boundary
- Multiclass classification
- Class membership probabilities
- Calibration (statistics)
- Concept drift
- Prior knowledge for pattern recognition
- Online Learning
- Margin Infused Relaxed Algorithm
- Semi-supervised learning
- Semi-supervised learning
- One-class classification
- Coupled pattern learner
- Lazy learning and nearest neighbors
- Lazy learning
- Eager learning
- Instance-based learning
- Cluster assumption
- K-nearest neighbor algorithm
- IDistance
- Large margin nearest neighbor
- Decision Trees
- Linear Classifiers
- Statistical classification
- Evaluation of Classification Models
- Data classification (business intelligence)
- Training set
- Test set
- Synthetic data
- Cross-validation (statistics)
- Loss function
- Hinge loss
- Generalization error
- Type I and type II errors
- Sensitivity and specificity
- Precision and recall
- F1 score
- Confusion matrix
- Matthews correlation coefficient
- Receiver operating characteristic
- Lift (data mining)
- Stability in learning
- Features Selection and Features Extraction
- Data Pre-processing
- Discretization of continuous features
- Feature selection
- Feature extraction
- Dimension reduction
- Principal component analysis
- Multilinear principal-component analysis
- Multifactor dimensionality reduction
- Targeted projection pursuit
- Multidimensional scaling
- Nonlinear dimensionality reduction
- Kernel principal component analysis
- Kernel eigenvoice
- Gramian matrix
- Gaussian process
- Kernel adaptive filter
- Isomap
- Manifold alignment
- Diffusion map
- Elastic map
- Locality-sensitive hashing
- Spectral clustering
- Minimum redundancy feature selection
- Clustering
- Rule Induction
- Association rules and Frequent Item Sets
- Ensemble Learning
- Ensemble learning
- Ensemble averaging
- Consensus clustering
- AdaBoost
- Boosting
- Bootstrap aggregating
- BrownBoost
- Cascading classifiers
- Co-training
- CoBoosting
- Gaussian process emulator
- Gradient boosting
- LogitBoost
- LPBoost
- Mixture model
- Product of Experts
- Random multinomial logit
- Random subspace method
- Weighted Majority Algorithm
- Randomized weighted majority algorithm