User:Dakror/Books/Machine Learning

Machine Learning

 * Introduction
 * Machine learning
 * Data mining


 * Problems
 * Statistical classification
 * Cluster analysis
 * Regression analysis
 * Anomaly detection
 * Association rule learning
 * Reinforcement learning
 * Structured prediction
 * Feature engineering
 * Feature learning
 * Online machine learning
 * Semi-supervised learning
 * Unsupervised learning
 * Learning to rank
 * Grammar induction


 * Supervised learning
 * Supervised learning
 * Decision tree learning
 * Ensemble learning
 * Bootstrap aggregating
 * Boosting (machine learning)
 * Random forest
 * K-nearest neighbors algorithm
 * Linear regression
 * Artificial neural network
 * Naive Bayes classifier
 * Perceptron
 * Logistic regression
 * Relevance vector machine
 * Support vector machine


 * Clustering
 * BIRCH
 * Hierarchical clustering
 * K-means clustering
 * Expectation–maximization algorithm
 * DBSCAN
 * OPTICS algorithm
 * Mean shift


 * Dimensionality reduction
 * Dimensionality reduction
 * Factor analysis
 * Canonical correlation
 * Independent component analysis
 * Linear discriminant analysis
 * Non-negative matrix factorization
 * Principal component analysis
 * T-distributed stochastic neighbor embedding


 * Structured prediction
 * Graphical model
 * Bayesian network
 * Conditional random field
 * Hidden Markov model


 * Anomaly detection
 * Local outlier factor


 * Neural nets
 * Autoencoder
 * Deep learning
 * Multilayer perceptron
 * Recurrent neural network
 * Restricted Boltzmann machine
 * Self-organizing map
 * Convolutional neural network


 * Theory
 * Bias–variance tradeoff
 * Computational learning theory
 * Empirical risk minimization
 * Occam learning
 * Probably approximately correct learning
 * Statistical learning theory
 * Vapnik–Chervonenkis theory