User:Oligenom/Books/ML2

An Overview

 * 01 - Introduction
 * Machine learning
 * Computational learning theory
 * Transduction (machine learning)
 * Artificial intelligence
 * Multi-task learning
 * Statistical classification
 * Structured prediction
 * Learning
 * Robot learning
 * Probably approximately correct learning
 * Empirical risk minimization
 * Unsupervised learning
 * Supervised learning
 * Semi-supervised learning
 * Reinforcement learning
 * Predictive modelling


 * 02 - ML- Approaches
 * List of machine learning algorithms
 * Decision tree
 * Decision tree learning
 * Boosting (machine learning)
 * K-nearest neighbors algorithm
 * Naive Bayes classifier
 * Logistic regression
 * Support vector machine
 * Artificial neural network
 * Bias-variance dilemma
 * Association rule learning
 * Inductive logic programming
 * Bayesian network
 * Similarity learning
 * Lazy learning


 * 03 - Other
 * Ensemble averaging
 * Recommender system
 * Directed acyclic graph
 * Conditional independence
 * Graphical model
 * Developmental robotics
 * Vapnik–Chervonenkis theory
 * Optical character recognition
 * Pattern recognition