Faul, A: Concise Introduction to Machine Learning (Chapman & Hall/Crc Machine Learning & Pattern Recognition)

✍ Scribed by A. C. Faul

• Publisher: Chapman and Hall/CRC
• Year: 2019
• Tongue: English
• Leaves: 335
• Category: Library

✦ Synopsis

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise.

This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
List of Figures
Preface
Acknowledgments
Chapter 1: Introduction
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 Probability Distributions
2.7 Graphical Representations
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
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
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
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
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
Chapter 8: Regression
8.1 Problem Description
8.2 Linear Regression
8.3 Polynomial Regression
8.4 Ordinary Least Squares
8.5 Over- and Under-fitting
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
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
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
Bibliography
Index