A concise introduction to machine learning:
Saved in:
| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
[2020]
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| Series: | Chapman & Hall/CRC machine learning & pattern recognition
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xviii, 314 Seiten Illustrationen, Diagramme |
| ISBN: | 9780815384106 9780815384205 |
Staff View
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Record in the Search Index
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| adam_text | Contents xi List of Figures Preface xvii Acknowledgments xix Chapter Chapter 2 ■ 2.1 1 1 ■ Introduction Probability Theory 7 ______ Independence, Probability Rules and Simpson’s Paradox 7 2.2 Probability Densities, Expectation, Variance and Moments 13 2.3 Examples of Discrete Probability Mass Functions 21 2.4 Examples of Continuous Probability Density Functions 29 2.5 Functions of Continuous Random Variables 46 2.6 Conjugate Probability Distributions 54 2.7 Graphical Representations 59 Chapter 3 ■ Sampling ______________ ___________ 63 3.1 Inverse Transform Sampling 3.2 Rejection Sampling 69 3.3 Importance Sampling 73 3.4 Markov Chains 75 3.5 Markov Chain Monte Carlo 82 Chapter 4 ■ Linear Classification 64 89 4.1 Features 89 4.2 Projections onto Subspaces 91 4.3 Fisher’s and Linear Discriminant Analysis 93 vii
viii ■ Contents 4.4 Multiple Classes 96 4.5 Online Learning and the Perceptron 99 4.6 The Support Vector Machine Chapter 5 ■ Non-Linear Classification 102 109 5.1 Quadratic Discriminant Analysis 109 5.2 Kernel Trick 112 5.3 к Nearest Neighbours 123 5.4 Decision Trees 123 5.5 Neural Networks 135 5.6 Boosting and Cascades 142 Chapter 6 ■ Clustering 149 6.1 AT Means Clustering 150 6.2 Mixture Models 152 6.3 Gaussian Mixture Models 156 6.4 Expectation-Maximization 162 6.5 Bayesian Mixture Models 166 6.6 The Chinese Restaurant Process 178 6.7 Dirichlet Process 181 Chapter 7 ■ Dimensionality Reduction 189 7.1 Principal Component Analysis 190 7.2 Probabilistic View 196 7.3 Expectation-Maximization 201 7.4 Factor Analysis 205 7.5 Kernel Principal Component Analysis 208 Chapter 8 ■ Regression 213 8.1 Problem Description 216 8.2 Linear Regression 217 8.3 Polynomial Regression 218 8.4 Ordinary Least Squares 220
Contents ■ ix 8.5 Over- and Under-fitting 222 8.6 Bias and Variance 226 8.7 Cross-validation 230 8.8 Multicollinearity and Principal Component Regression 230 8.9 Partial Least Squares 233 8.10 Regularization 234 8.11 Bayesian Regression 238 8.12 Expectation-Maximization 239 8.13 Bayesian Learning 241 8.14 Gaussian Process 253 Chapter 9 ■ Feature Learning 9.1 ______ 263 Neural Networks 264 9.2 Error Backpropagation 271 9.3 Autoencoders 277 9.4 Autoencoder Example 284 9.5 Relationship to Other Techniques 290 9.6 Indian Buffet Process 293 Appendix A: Matrix Formulae A.1 A.2 297 Determinants and Inverses 297 A. 1.1 A.1.2 A. 1.3 A. 1.4 A. 1.5 297 297 297 297 298 Block Matrix Inversion Block Matrix Determinant Woodbury Identity Sherman-Morrison Formula Matrix Determinant Lemma Derivatives A.2.1 A.2.2 A.2.3 A.2.4 A.2.5 A.2.6 A.2.7 A.2.8 Derivative Derivative Derivative Derivative Derivative Derivative Derivative Derivative 298 of Squared Norm of Inner Product of Second Order Vector Product of Determinant of Matrix Times Vectors of Transpose Matrix Times Vectors of Inverse of Inverse Times Vectors 298 298 298 298 299 299 299 299
x ■ Contents A.2.9 Derivative of Trace of Second Order Products A.2.10 Derivative of Trace of Product with Diagonal Matrix 299 299 Bibliography___________________ _________ 301 Index 305
A Concise Introduction to Machine Learning uses mathematics as the common language to explain a variety of machine learning concepts from basic principles, and illustrates every concept using examples in MATLAB®. 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 dif ferences 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 em ploying machine learning techniques. About the Author A.C. Faul was a Teaching Associate, Fellow and Director of Studies in Mathemat ics at Selwyn College, University of Cambridge. She came to Cambridge after studying two years in Germany. She did Part II and Part III Mathematics at Churchill College, Cambridge. Since these are only two years, and three years are necessary for a first degree, she does not hold one. However, this was followed by a PhD on the Faul-Powell Algorithm for Radial Basis Function Interpolation under the supervis ion of Professor Mike Powell. She then worked on the Relevance Vector Machine with Mike Tipping at Microsoft Research Cambridge. Ten years in indus try followed where she worked on various algorithms on mobile phone networks, image processing and data visualization. Current projects are on machine learning techniques. In teaching, she enjoys to bring out the
underlying, connecting prin ciples of algorithms, which is the emphasis of a book on Numerical Analysis she has written.
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| spelling | Faul, Anita Verfasser (DE-588)1150897651 aut A concise introduction to machine learning A. C. Faul Boca Raton ; London ; New York CRC Press [2020] xviii, 314 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC machine learning & pattern recognition Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s DE-604 Erscheint auch als Online-Ausgabe 978-1-351-20475-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031544401&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031544401&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
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