User:Curiousguy13/Books/Machine learning and Data Mining

compiled by Kunal Arora

 * Introduction
 * Machine learning
 * Data mining
 * Pattern recognition


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


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


 * Clustering
 * BIRCH (data clustering)
 * Hierarchical clustering
 * K-means clustering
 * Expectation–maximization algorithm
 * DBSCAN
 * OPTICS algorithm
 * Mean-shift
 * Supervised learning


 * 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
 * K-nearest neighbors algorithm


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


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