Machine learning: a first course for engineers and scientists
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| Main Authors: | , , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge, UK ; New York, NY
Cambridge University Press
2022
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverzeichnis Seite 327-334 Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | xii, 338 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108843607 1108843603 |
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| 245 | 1 | 0 | |a Machine learning |b a first course for engineers and scientists |c Andreas Lindholm (Annotell, Sweden), Niklas Wahlström (Uppsala University, Sweden), Fredrik Lindsten (Linköping University, Sweden), Thomas B. Schön (Uppsala University, Sweden) |
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Contents Acknowledgements ix Notation xi 1 Introduction 1.1 1.2 1.3 2 3 Supervised Learning: A First Approach 2.1 Supervised Machine Learning. 2.2 A Distance-Based Method: k-NN. 2.3 A Rule-Based Method: Decision Trees. 2.4 Further Reading. Basic Parametric Models and a Statistical Perspective on Learning 3.1 3.2 3.3 3.4 3.5 3.A 4 5 Machine Learning Exemplified. About This Book. Further Reading. Linear Regression. Classification and Logistic Regression. Polynomial Regression and Régularisation. Generalised Linear Models. Further Reading. Derivation of the Normal Equations. 1 2 10 12 13 13 19 25 36 37 37 45 54 57 60 60 Understanding, Evaluating, and Improving Performance 4.1 Expected New Data Error Enew: Performance in Production . 63 63 4.2 4.3 4.4 4.5 4.6 66 71 79 86 90
Estimating Enew. The Training Error-Generalisation Gap Decomposition of Anew . . The Bias-Variance Decomposition of Enew. Additional Tools for Evaluating Binary Classifiers. Further Reading. Learning Parametric Models 5.1 5.2 5.3 5.4 5.5 Principles of Parametric Modelling. Loss Functions and Likelihood-Based Models. Régularisation. Parameter Optimisation. Optimisation with Large Datasets. 91 91 96 109 112 124 v
Contents 5.6 5.7 6 Hyperparameter Optimisation . Further Reading. 129 131 Neural Networks and Deep Learning 6.1 The Neural Network Model. 133 6.2 6.3 6.4 6.5 6.A 7 8 Training a Neural Network. Convolutional Neural Networks . Dropout. Further Reading. Derivation of the Backpropagation Equations. 133 140 147 155 159 160 Ensemble Methods: Bagging and Boosting 163 7.1 7.2 7.3 7.4 7.5 Bagging. Random Forests. Boosting and AdaBoost. Gradient Boosting. Further Reading. 164 171 174 182 187 Non-linear Input Transformations and Kernels 8.1 Creating Features by Non-linear Input Transformations. 189 The Bayesian Approach and Gaussian Processes 217 189 8.2 Kernel Ridge
Regression. 192 8.3 Support Vector Regression. 197 8.4 Kernel Theory. 202 8.5 Support Vector Classification. 208 8.6 Further Reading. 213 8.A The Representer Theorem. 213 8.B Derivation of Support Vector Classification. 214 9 9.1 9.2 9.3 9.4 9.5 9.6 9.A The Bayesian Idea. 217 Bayesian Linear Regression.220 The Gaussian Process. 226 Practical Aspects of the Gaussian Process. 237 Other Bayesian Methods in Machine Learning. 242 Further Reading. 242 The Multivariate Gaussian Distribution . 243 10 Generative Models and Learning from Unlabelled Data 247 10.1 The Gaussian Mixture Model and Discriminant Analysis. 248 10.2 10.3 10.4 10.5 vi Cluster
Analysis. 259 Deep Generative Models. 268 Representation Learning and Dimensionality Reduction. 275 Further Reading. 285
Contents 11 User Aspects of Machine Learning 287 11.1 Defining the Machine Learning Problem. 287 11.2 Improving a Machine Learning Model. 291 11.3 What If We Cannot Collect More Data?. 299 11.4 Practical Data Issues. 303 11.5 Can I Trust my Machine Learning Model?.307 11.6 Further Reading. 308 12 Ethics in Machine Learning 309 12.1 Fairness and Error Functions. 309 12.2 Misleading Claims about Performance. 314 12.3 Limitations of Training Data.322 12.4 Further Reading. 326 Bibliography 327 Index 335 vii |
| adam_txt |
Contents Acknowledgements ix Notation xi 1 Introduction 1.1 1.2 1.3 2 3 Supervised Learning: A First Approach 2.1 Supervised Machine Learning. 2.2 A Distance-Based Method: k-NN. 2.3 A Rule-Based Method: Decision Trees. 2.4 Further Reading. Basic Parametric Models and a Statistical Perspective on Learning 3.1 3.2 3.3 3.4 3.5 3.A 4 5 Machine Learning Exemplified. About This Book. Further Reading. Linear Regression. Classification and Logistic Regression. Polynomial Regression and Régularisation. Generalised Linear Models. Further Reading. Derivation of the Normal Equations. 1 2 10 12 13 13 19 25 36 37 37 45 54 57 60 60 Understanding, Evaluating, and Improving Performance 4.1 Expected New Data Error Enew: Performance in Production . 63 63 4.2 4.3 4.4 4.5 4.6 66 71 79 86 90
Estimating Enew. The Training Error-Generalisation Gap Decomposition of Anew . . The Bias-Variance Decomposition of Enew. Additional Tools for Evaluating Binary Classifiers. Further Reading. Learning Parametric Models 5.1 5.2 5.3 5.4 5.5 Principles of Parametric Modelling. Loss Functions and Likelihood-Based Models. Régularisation. Parameter Optimisation. Optimisation with Large Datasets. 91 91 96 109 112 124 v
Contents 5.6 5.7 6 Hyperparameter Optimisation . Further Reading. 129 131 Neural Networks and Deep Learning 6.1 The Neural Network Model. 133 6.2 6.3 6.4 6.5 6.A 7 8 Training a Neural Network. Convolutional Neural Networks . Dropout. Further Reading. Derivation of the Backpropagation Equations. 133 140 147 155 159 160 Ensemble Methods: Bagging and Boosting 163 7.1 7.2 7.3 7.4 7.5 Bagging. Random Forests. Boosting and AdaBoost. Gradient Boosting. Further Reading. 164 171 174 182 187 Non-linear Input Transformations and Kernels 8.1 Creating Features by Non-linear Input Transformations. 189 The Bayesian Approach and Gaussian Processes 217 189 8.2 Kernel Ridge
Regression. 192 8.3 Support Vector Regression. 197 8.4 Kernel Theory. 202 8.5 Support Vector Classification. 208 8.6 Further Reading. 213 8.A The Representer Theorem. 213 8.B Derivation of Support Vector Classification. 214 9 9.1 9.2 9.3 9.4 9.5 9.6 9.A The Bayesian Idea. 217 Bayesian Linear Regression.220 The Gaussian Process. 226 Practical Aspects of the Gaussian Process. 237 Other Bayesian Methods in Machine Learning. 242 Further Reading. 242 The Multivariate Gaussian Distribution . 243 10 Generative Models and Learning from Unlabelled Data 247 10.1 The Gaussian Mixture Model and Discriminant Analysis. 248 10.2 10.3 10.4 10.5 vi Cluster
Analysis. 259 Deep Generative Models. 268 Representation Learning and Dimensionality Reduction. 275 Further Reading. 285
Contents 11 User Aspects of Machine Learning 287 11.1 Defining the Machine Learning Problem. 287 11.2 Improving a Machine Learning Model. 291 11.3 What If We Cannot Collect More Data?. 299 11.4 Practical Data Issues. 303 11.5 Can I Trust my Machine Learning Model?.307 11.6 Further Reading. 308 12 Ethics in Machine Learning 309 12.1 Fairness and Error Functions. 309 12.2 Misleading Claims about Performance. 314 12.3 Limitations of Training Data.322 12.4 Further Reading. 326 Bibliography 327 Index 335 vii |
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| author | Lindholm, Andreas Wahlström, Niklas 1984- Lindsten, Fredrik Schön, Thomas B. 1977- |
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| discipline | Informatik |
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| illustrated | Illustrated |
| index_date | 2024-07-03T19:48:09Z |
| indexdate | 2025-02-20T07:15:04Z |
| institution | BVB |
| isbn | 9781108843607 1108843603 |
| language | English |
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| physical | xii, 338 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
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| publisher | Cambridge University Press |
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| spellingShingle | Lindholm, Andreas Wahlström, Niklas 1984- Lindsten, Fredrik Schön, Thomas B. 1977- Machine learning a first course for engineers and scientists Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4193754-5 |
| title | Machine learning a first course for engineers and scientists |
| title_auth | Machine learning a first course for engineers and scientists |
| title_exact_search | Machine learning a first course for engineers and scientists |
| title_exact_search_txtP | Machine learning a first course for engineers and scientists |
| title_full | Machine learning a first course for engineers and scientists Andreas Lindholm (Annotell, Sweden), Niklas Wahlström (Uppsala University, Sweden), Fredrik Lindsten (Linköping University, Sweden), Thomas B. Schön (Uppsala University, Sweden) |
| title_fullStr | Machine learning a first course for engineers and scientists Andreas Lindholm (Annotell, Sweden), Niklas Wahlström (Uppsala University, Sweden), Fredrik Lindsten (Linköping University, Sweden), Thomas B. Schön (Uppsala University, Sweden) |
| title_full_unstemmed | Machine learning a first course for engineers and scientists Andreas Lindholm (Annotell, Sweden), Niklas Wahlström (Uppsala University, Sweden), Fredrik Lindsten (Linköping University, Sweden), Thomas B. Schön (Uppsala University, Sweden) |
| title_short | Machine learning |
| title_sort | machine learning a first course for engineers and scientists |
| title_sub | a first course for engineers and scientists |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Maschinelles Lernen |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033590490&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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Indexes
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 ST 300 L745 |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |