User:Regoldo01/Books/Machine Part 1

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

Evaluation of Clustering Methods

Rand index

Dunn index

Davies–Bouldin index

Jaccard index

MinHash

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

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

Graphical Models

Graphical model

State transition network

Bayesian Learning Methods

Naive Bayes classifier

Averaged one-dependence estimators

Bayesian network

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

Learning Theory

Computational learning theory

Version space

Probably approximately correct learning

Vapnik–Chervonenkis theory

Shattering (machine learning)

VC dimension

Minimum description length

Bondy's theorem

Inferential theory of learning

Rademacher complexity

Teaching dimension

Subclass reachability

Sample exclusion dimension

Unique negative dimension

Uniform convergence (combinatorics)

Witness set

Support Vector Machines

Kernel methods

Support vector machine

Structural risk minimization

Empirical risk minimization

Kernel trick

Least squares support vector machine

Relevance vector machine

Sequential minimal optimization

Structured SVM

Regression analysis

Outline of regression analysis

Regression analysis

Dependent and independent variables

Linear model

Linear regression

Least squares

Linear least squares (mathematics)

Local regression

Additive model

Antecedent variable

Autocorrelation

Backfitting algorithm

Bayesian linear regression

Bayesian multivariate linear regression

Binomial regression

Canonical analysis

Censored regression model

Coefficient of determination

Comparison of general and generalized linear models

Compressed sensing

Conditional change model

Controlling for a variable

Cross-sectional regression

Curve fitting

Deming regression

Design matrix

Difference in differences

Dummy variable (statistics)

Errors and residuals in statistics

Errors-in-variables models

Explained sum of squares

Explained variation

First-hitting-time model

Fixed effects model

Fraction of variance unexplained

Frisch–Waugh–Lovell theorem

General linear model

Generalized additive model

Generalized additive model for location, scale and shape

Generalized estimating equation

Generalized least squares

Generalized linear array model

Generalized linear mixed model

Generalized linear model

Growth curve

Guess value

Hat matrix

Heckman correction

Heteroscedasticity-consistent standard errors

Hosmer–Lemeshow test

Instrumental variable

Interaction (statistics)

Isotonic regression

Iteratively reweighted least squares

Kitchen sink regression

Lack-of-fit sum of squares

Leverage (statistics)

Limited dependent variable

Linear probability model

Mallows's C_p

Mean and predicted response

Mixed model

Moderation (statistics)

Moving least squares

Multicollinearity

Multiple correlation

Multivariate probit

Multivariate adaptive regression splines

Newey–West estimator

Non-linear least squares

Nonlinear regression

Logistic Regression

Logit

Multinomial logit

Logistic regression

Bio-inspired Methods

Bio-inspired computing

Evolutionary Algorithms

Evolvability (computer science)

Evolutionary computation

Evolutionary algorithm

Genetic algorithm

Chromosome (genetic algorithm)

Crossover (genetic algorithm)

Fitness function

Evolutionary data mining

Genetic programming

Learnable Evolution Model