User:Mathurin.ache/Books/Machine Learning v1

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

Reasoning

Abductive reasoning

Inductive reasoning

First-order logic

Inductive logic programming

Reasoning system

Case-based reasoning

Textual case based reasoning

Causality

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

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

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

CHAID

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

Isomap

Manifold alignment

Diffusion map

Elastic map

Locality-sensitive hashing

Spectral clustering

Minimum redundancy feature selection

Clustering

Cluster analysis

K-means clustering

K-means++

K-medians clustering

K-medoids

DBSCAN

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

Dendrogram

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

SUBCLU

Thresholding (image processing)

UPGMA