User:Taser/Books/Machine Learning Vol 1

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Machine Learning[edit]

Introduction and Main Principles
Machine learning
Data analysis
Occam's razor
Curse of dimensionality
No free lunch theorem
Accuracy paradox
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
Abductive reasoning
Inductive reasoning
First-order logic
Inductive logic programming
Reasoning system
Case-based reasoning
Textual case based reasoning
Search Methods
Nearest neighbor search
Stochastic gradient descent
Beam search
Best-first search
Breadth-first search
Hill climbing
Grid search
Brute-force search
Depth-first search
Tabu search
Anytime algorithm
Exploratory data analysis
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
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
Iris flower data set
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
Large margin nearest neighbor
Decision Trees
Decision tree learning
Decision stump
Pruning (decision trees)
Mutual information
Adjusted mutual information
Information gain ratio
Information gain in decision trees
ID3 algorithm
C4.5 algorithm
Information Fuzzy Networks
Grafting (decision trees)
Incremental decision tree
Alternating decision tree
Logistic model tree
Random forest
Linear Classifiers
Linear classifier
Margin (machine learning)
Margin classifier
Soft independent modelling of class analogies
Statistical classification
Statistical classification
Probability matching
Discriminative model
Linear discriminant analysis
Multiclass LDA
Multiple discriminant analysis
Optimal discriminant analysis
Fisher kernel
Discriminant function analysis
Multilinear subspace learning
Quadratic classifier
Variable kernel density estimation
Category utility
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
Manifold alignment
Diffusion map
Elastic map
Locality-sensitive hashing
Spectral clustering
Minimum redundancy feature selection
Cluster analysis
K-means clustering
K-medians clustering
Fuzzy clustering
BIRCH (data clustering)
Canopy clustering algorithm
Cluster-weighted modeling
Clustering high-dimensional data
Cobweb (clustering)
Complete-linkage clustering
Constrained clustering
Correlation clustering
CURE data clustering algorithm
Data stream clustering
Determining the number of clusters in a data set
FLAME clustering
Hierarchical clustering
Information bottleneck method
Lloyd's algorithm
Nearest-neighbor chain algorithm
Neighbor joining
OPTICS algorithm
Pitman–Yor process
Single-linkage clustering
Thresholding (image processing)
Evaluation of Clustering Methods
Rand index
Dunn index
Davies–Bouldin index
Jaccard index
K q-flats
Rule Induction
Decision rules
Rule induction
Classification rule
CN2 algorithm
Decision list
First Order Inductive Learner
Association rules and Frequent Item Sets
Association rule learning
Apriori algorithm
Contrast set learning
Affinity analysis
K-optimal pattern discovery
Ensemble Learning
Ensemble learning
Ensemble averaging
Consensus clustering
Bootstrap aggregating
Cascading classifiers
Gaussian process emulator
Gradient boosting
Mixture model
Product of Experts
Random multinomial logit
Random subspace method
Weighted Majority Algorithm
Randomized weighted majority algorithm
Graphical Models
Graphical model
State transition network
Bayesian Learning Methods
Naive Bayes classifier
Averaged one-dependence estimators
Bayesian network
Bayesian additive regression kernels
Variational message passing
Markov Models
Markov model
Maximum-entropy Markov model
Hidden Markov model
Baum–Welch algorithm
Forward–backward algorithm
Hierarchical hidden Markov model
Markov logic network
Markov chain Monte Carlo
Markov random field
Conditional random field
Predictive state representation