What Is Machine Learning? – Examples of Machine Learning Applications – Supervised Learning: Vapnik-Chervonenkis (VC) Dimension - Probably Approximately Correct (PAC) Learning - Regression - Model Selection and Generalization - Dimensions of a Supervised Machine Learning Algorithm

Introduction – Classification - Losses and Risks – Discriminant Functions - Association Rules – Parametric Methods: Introduction - Maximum Likelihood Estimation - Evaluating an Estimator: Bias and Variance - The Bayes’ Estimator - Parametric Classification – Regression - Multivariate Methods: Multivariate Data - Parameter Estimation - Estimation of Missing Values - Multivariate Normal Distribution - Multivariate Classification - Tuning Complexity - Discrete Features - Multivariate Regression

Introduction - Mixture Densities - k-Means Clustering - Expectation-Maximization Algorithm - Supervised Learning after Clustering - Hierarchical Clustering - Nonparametric Methods: Introduction - Nonparametric Density Estimation - Generalization to Multivariate Data - Nonparametric Classification - Condensed Nearest Neighbor - Nonparametric Regression: Smoothing Models

Introduction - Univariate Trees – Pruning - Rule Extraction from Trees - Learning Rules from Data - Multivariate Trees - Linear Discrimination: Introduction - Generalizing the Linear Model - Geometry of the Linear Discriminant - Pairwise Separation - Parametric Discrimination Revisited - Gradient Descent - Logistic Discrimination - Discrimination by Regression

Introduction - The Perceptron - Training a Perceptron - Learning Boolean Functions - Multilayer Perceptrons - MLP as a Universal Approximator - Backpropagation Algorithm - Training Procedures - Dimensionality Reduction - Hidden Markov Models: Discrete Markov Processes - Hidden Markov Models - Three Basic Problems of HMMs - Learning Model Parameters - The HMM with Input