User:Mathurin.ache/Books/MachineLearningv1

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