User:Yahassine/Books/Machine Learning: The Complete Guide Part I

Machine Learning: The Complete Guide Part I

 * Introduction and Main Principles
 * Machine learning
 * Data analysis
 * Occam's razor
 * Curse of dimensionality
 * No free lunch theorem
 * Accuracy paradox
 * Overfitting
 * Regularization (mathematics)
 * Inductive bias
 * Data dredging
 * Ugly duckling theorem
 * Uncertain data


 * Background and Preliminaries
 * Knowledge discovery in Databases
 * Knowledge extraction
 * Data mining
 * Predictive analytics
 * Predictive modelling
 * Business intelligence
 * Reactive business intelligence
 * LIONsolver
 * Business analytics
 * 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
 * Brute-force search
 * Depth-first search
 * Tabu search
 * Anytime algorithm


 * Introduction and Main Principles
 * Machine learning
 * Data analysis
 * Occam's razor
 * Curse of dimensionality
 * No free lunch theorem
 * Accuracy paradox
 * Overfitting
 * Regularization (mathematics)
 * Inductive bias
 * Data dredging
 * Ugly duckling theorem
 * Uncertain data


 * Background and Preliminaries
 * Knowledge discovery in Databases
 * Knowledge extraction
 * Data mining
 * Predictive analytics
 * Predictive modelling
 * Business intelligence
 * Reactive business intelligence
 * LIONsolver
 * Business analytics
 * 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
 * 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 (machine learning)
 * Explanation-based learning
 * Offline learning


 * Introduction and Main Principles
 * Machine learning
 * Data analysis
 * Occam's razor
 * Curse of dimensionality
 * No free lunch theorem
 * Accuracy paradox
 * Overfitting
 * Regularization (mathematics)
 * Inductive bias
 * Data dredging
 * Ugly duckling theorem
 * Uncertain data


 * Background and Preliminaries
 * Knowledge discovery in Databases
 * Knowledge extraction
 * Data mining
 * Predictive analytics
 * Predictive modelling
 * Business intelligence
 * Reactive business intelligence
 * LIONsolver
 * Business analytics
 * 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
 * Brute-force search
 * Depth-first search
 * Tabu search
 * Anytime algorithm


 * Introduction and Main Principles
 * Machine learning
 * Data analysis
 * Occam's razor
 * Curse of dimensionality
 * No free lunch theorem
 * Accuracy paradox
 * Overfitting
 * Regularization (mathematics)
 * Inductive bias
 * Data dredging
 * Ugly duckling theorem
 * Uncertain data


 * Background and Preliminaries
 * Knowledge discovery in Databases
 * Knowledge extraction
 * Data mining
 * Predictive analytics
 * Predictive modelling
 * Business intelligence
 * Reactive business intelligence
 * LIONsolver
 * Business analytics
 * 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
 * Hyperparameter optimization
 * 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 (machine learning)
 * Explanation-based learning
 * Offline learning
 * Online machine learning
 * Hyperparameter optimization