User:Gk.mansoor/Books/Machine Learning Algorithms - An Overview

An Overview

 * Introduction
 * Class membership probabilities
 * Computational learning theory
 * Data mining
 * Inductive bias
 * Machine learning
 * Overfitting
 * Version space


 * Supervised Learning - Types
 * Active learning (machine learning)
 * Learning to rank
 * Semi-supervised learning
 * Structured prediction
 * Supervised learning


 * Supervised Learning Algorithms
 * Backpropagation
 * Boosting (machine learning)
 * Case-based reasoning
 * Data pre-processing
 * Decision tree learning
 * Ensemble learning
 * Inductive logic programming
 * K-nearest neighbors algorithm
 * Kriging
 * Learning automata
 * Level of measurement
 * Minimum message length
 * Multilinear subspace learning
 * Proaftn
 * Probably approximately correct learning
 * Random forest
 * Ripple-down rules
 * Similarity learning
 * Statistical relational learning
 * Support vector machine
 * Variable kernel density estimation


 * Supervised Learning - Bayesian Statistics
 * Bayesian network
 * Bayesian statistics
 * Naive Bayes classifier


 * Unsupervised Learning - Types
 * Adaptive resonance theory
 * Artificial neural network
 * Blind signal separation
 * Cluster analysis
 * Hidden Markov model
 * Self-organizing map
 * Unsupervised learning


 * Transduction
 * Transduction (machine learning)


 * Reinforcement Learning - Types
 * Dynamic treatment regime
 * Error-driven learning
 * Fictitious play
 * Learning classifier system
 * Optimal control
 * Q-learning
 * Reinforcement learning
 * SARSA
 * Temporal difference learning


 * Inductive Transfer
 * Inductive transfer
 * Multi-task learning


 * Association Rule Mining
 * Association rule learning


 * Manifold Learning
 * Nonlinear dimensionality reduction


 * Deep Learning
 * Deep learning