A Concise Introduction to Machine Learning (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Cover Page
Endorsements Page
Half-Title Page
Series Page
Title Page
Copyright Page
Dedication Page
Contents
List of Figures
List of Listings
Foreword
Preface
Acknowledgments
Chapter 1 Introduction
	Bibliography
Chapter 2 Probability Theory
	2.1 Independence, Probability Rules and Simpson's Paradox
	2.2 Probability Densities, Expectation, Variance and Moments
	2.3 Examples of Discrete Probability Mass Functions
	2.4 Examples of Continuous Probability Density Functions
	2.5 Functions of Continuous Random Variables
	2.6 Conjugate, Prior and Posterior Distributions and Maximum Likelihood
	2.7 Graphical Representations
	Exercises
	Bibliography
Chapter 3 Sampling
	3.1 Inverse Transform Sampling
	3.2 Rejection Sampling
	3.3 Importance Sampling
	3.4 Markov Chains
	3.5 Markov Chain Monte Carlo
	Exercises
	Bibliography
Chapter 4 Linear Classification
	4.1 Features
	4.2 Projections onto Subspaces
	4.3 Fisher's and Linear Discriminant Analysis
	4.4 Multiple Classes
	4.5 Online Learning and the Perceptron
	4.6 The Support Vector Machine
	Exercises
	Bibliography
Chapter 5 Non-Linear Classification
	5.1 Quadratic Discriminant Analysis
	5.2 Kernel Trick
	5.3 k Nearest Neighbours
	5.4 Decision Trees
	5.5 Neural Networks
	5.6 Boosting and Cascades
	Exercises
	Bibliography
Chapter 6 Clustering
	6.1 K Means Clustering
	6.2 Mixture Models
	6.3 Gaussian Mixture Models
	6.4 Expectation-Maximization
	6.5 Bayesian Mixture Models
	6.6 The Chinese Restaurant Process
	6.7 Dirichlet Process
	Exercises
	Bibliography
Chapter 7 Dimensionality Reduction
	7.1 Principal Component Analysis
	7.2 Probabilistic View
	7.3 Expectation-Maximization
	7.4 Factor Analysis
	7.5 Kernel Principal Component Analysis
	Exercises
	Bibliography
Chapter 8 Regression
	8.1 Problem description
	8.2 Linear Regression
	8.3 Polynomial Regression
	8.4 Ordinary Least Squares
	8.5 Over- and Underfitting
	8.6 Bias and Variance
	8.7 Cross-validation
	8.8 Multicollinearity and Principal Component Regression
	8.9 Partial Least Squares
	8.10 Regularization
	8.11 Bayesian Regression
	8.12 Expectation–Maximization
	8.13 Bayesian Learning
	8.14 Gaussian Process
	Exercises
	Bibliography
Chapter 9 Feature Learning
	9.1 Neural Networks
	9.2 Error Backpropagation
	9.3 Autoencoders
	9.4 Autoencoder Example
	9.5 Relationship to Other Techniques
	9.6 Indian Buffet Process
	Exercises
	Bibliography
Appendix A Matrix Formulae
	A.1 Determinants and inverses
		A.1.1 Block Matrix Inversion
		A.1.2 Block Matrix Determinant
		A.1.3 Woodbury Identity
		A.1.4 Sherman–Morrison Formula
		A.1.5 Matrix Determinant Lemma
	A.2 Derivatives
		A.2.1 Derivative of squared norm
		A.2.2 Derivative of inner product
		A.2.3 Derivative of second order vector product
		A.2.4 Derivative of determinant
		A.2.5 Derivative of matrix times vectors
		A.2.6 Derivative of transpose matrix times vectors
		A.2.7 Derivative of inverse
		A.2.8 Derivative of inverse times vectors
		A.2.9 Derivative of trace of second order products
		A.2.10 Derivative of trace of product with diagonal matrix
Index