A concise introduction to machine learning

✍ Scribed by Faul A.C

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 335
• Category: Library

✦ Table of Contents

Cover......Page 1
Half Title......Page 2
Series Page......Page 3
Title Page......Page 4
Copyright Page......Page 5
Dedication......Page 6
Contents......Page 8
List of Figures......Page 12
Preface......Page 18
Acknowledgments......Page 20
Chapter 1: Introduction......Page 22
2.1 Independence, Probability Rules and Simpson’s Paradox......Page 28
2.2 Probability Densities, Expectation, Variance and Moments......Page 34
2.3 Examples of Discrete Probability Mass Functions......Page 42
2.4 Examples of Continuous Probability Density Functions......Page 50
2.5 Functions of Continuous Random Variables......Page 67
2.6 Conjugate Probability Distributions......Page 75
2.7 Graphical Representations......Page 80
Chapter 3: Sampling......Page 84
3.1 Inverse Transform Sampling......Page 85
3.2 Rejection Sampling......Page 90
3.3 Importance Sampling......Page 94
3.4 Markov Chains......Page 96
3.5 Markov Chain Monte Carlo......Page 103
4.1 Features......Page 110
4.2 Projections onto Subspaces......Page 112
4.3 Fisher’s and Linear Discriminant Analysis......Page 114
4.4 Multiple Classes......Page 117
4.5 Online Learning and the Perceptron......Page 120
4.6 The Support Vector Machine......Page 123
5.1 Quadratic Discriminant Analysis......Page 130
5.2 Kernel Trick......Page 133
5.4 Decision Trees......Page 144
5.5 Neural Networks......Page 156
5.6 Boosting and Cascades......Page 163
Chapter 6: Clustering......Page 170
6.1 K Means Clustering......Page 171
6.2 Mixture Models......Page 173
6.3 Gaussian Mixture Models......Page 177
6.4 Expectation-Maximization......Page 183
6.5 Bayesian Mixture Models......Page 187
6.6 The Chinese Restaurant Process......Page 199
6.7 Dirichlet Process......Page 202
Chapter 7: Dimensionality Reduction......Page 210
7.1 Principal Component Analysis......Page 211
7.2 Probabilistic View......Page 217
7.3 Expectation-Maximization......Page 222
7.4 Factor Analysis......Page 226
7.5 Kernel Principal Component Analysis......Page 229
Chapter 8: Regression......Page 234
8.1 Problem Description......Page 237
8.2 Linear Regression......Page 238
8.3 Polynomial Regression......Page 239
8.4 Ordinary Least Squares......Page 241
8.5 Over- and Under-fitting......Page 243
8.6 Bias and Variance......Page 247
8.8 Multicollinearity and Principal Component Regression......Page 251
8.9 Partial Least Squares......Page 254
8.10 Regularization......Page 255
8.11 Bayesian Regression......Page 259
8.12 Expectation–Maximization......Page 260
8.13 Bayesian Learning......Page 262
8.14 Gaussian Process......Page 274
Chapter 9: Feature Learning......Page 284
9.1 Neural Networks......Page 285
9.2 Error Backpropagation......Page 292
9.3 Autoencoders......Page 298
9.4 Autoencoder Example......Page 305
9.5 Relationship to Other Techniques......Page 311
9.6 Indian Buffet Process......Page 314
A.1.4 Sherman–Morrison Formula......Page 318
A.2.4 Derivative of Determinant......Page 319
A.2.10 Derivative of Trace of Product with Diagonal Matrix......Page 320
Bibliography......Page 322
Index......Page 326