Machine Learning Refined: Foundations, Algorithms, and Applications, 2nd Edition
- Length: 594 pages
- Edition: 2
- Language: English
- Publisher: Cambridge University Press
- Publication Date: 2020-03-12
- ISBN-10: 1108480721
- ISBN-13: 9781108480727
- Sales Rank: #89119 (See Top 100 Books)
With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
Cover page Half Title page Title page Copyright page Dedication Contents Preface Acknowledgements 1 Introduction to Machine Learning 1.1 Introduction 1.2 Distinguishing Cats from Dogs: a Machine Learning Approach 1.3 The Basic Taxonomy of Machine Learning Problems 1.4 Mathematical Optimization 1.5 Conclusion Part I Mathematical Optimization 2 Zero-Order Optimization Techniques 2.1 Introduction 2.2 The Zero-Order Optimality Condition 2.3 Global Optimization Methods 2.4 Local Optimization Methods 2.5 Random Search 2.6 Coordinate Search and Descent 2.7 Conclusion 2.8 Exercises 3 First-Order Optimization Techniques 3.1 Introduction 3.2 The First-Order Optimality Condition 3.3 The Geometry of First-Order Taylor Series 3.4 Computing Gradients Efficiently 3.5 Gradient Descent 3.6 Two Natural Weaknesses of Gradient Descent 3.7 Conclusion 3.8 Exercises 4 Second-Order Optimization Techniques 4.1 The Second-Order Optimality Condition 4.2 The Geometry of Second-Order Taylor Series 4.3 Newton’s Method 4.4 Two Natural Weaknesses of Newton’s Method 4.5 Conclusion 4.6 Exercises Part II Linear Learning 5 Linear Regression 5.1 Introduction 5.2 Least Squares Linear Regression 5.3 Least Absolute Deviations 5.4 Regression Quality Metrics 5.5 Weighted Regression 5.6 Multi-Output Regression 5.7 Conclusion 5.8 Exercises 5.9 Endnotes 6 Linear Two-Class Classification 6.1 Introduction 6.2 Logistic Regression and the Cross Entropy Cost 6.3 Logistic Regression and the Softmax Cost 6.4 The Perceptron 6.5 Support Vector Machines 6.6 Which Approach Produces the Best Results? 6.7 The Categorical Cross Entropy Cost 6.8 Classification Quality Metrics 6.9 Weighted Two-Class Classification 6.10 Conclusion 6.11 Exercises 7 Linear Multi-Class Classification 7.1 Introduction 7.2 One-versus-All Multi-Class Classification 7.3 Multi-Class Classification and the Perceptron 7.4 Which Approach Produces the Best Results? 7.5 The Categorical Cross Entropy Cost Function 7.6 Classification Quality Metrics 7.7 Weighted Multi-Class Classification 7.8 Stochastic and Mini-Batch Learning 7.9 Conclusion 7.10 Exercises 8 Linear Unsupervised Learning 8.1 Introduction 8.2 Fixed Spanning Sets, Orthonormality, and Projections 8.3 The Linear Autoencoder and Principal Component Analysis 8.4 Recommender Systems 8.5 K-Means Clustering 8.6 General Matrix Factorization Techniques 8.7 Conclusion 8.8 Exercises 8.9 Endnotes 9 Feature Engineering and Selection 9.1 Introduction 9.2 Histogram Features 9.3 Feature Scaling via Standard Normalization 9.4 Imputing Missing Values in a Dataset 9.5 Feature Scaling via PCA-Sphering 9.6 Feature Selection via Boosting 9.7 Feature Selection via Regularization 9.8 Conclusion 9.9 Exercises Part III Nonlinear Learning 10 Principles of Nonlinear Feature Engineering 10.1 Introduction 10.2 Nonlinear Regression 10.3 Nonlinear Multi-Output Regression 10.4 Nonlinear Two-Class Classification 10.5 Nonlinear Multi-Class Classification 10.6 Nonlinear Unsupervised Learning 10.7 Conclusion 10.8 Exercises 11 Principles of Feature Learning 11.1 Introduction 11.2 Universal Approximators 11.3 Universal Approximation of Real Data 11.4 Naive Cross-Validation 11.5 Efficient Cross-Validation via Boosting 11.6 Efficient Cross-Validation via Regularization 11.7 Testing Data 11.8 Which Universal Approximator Works Best in Practice? 11.9 Bagging Cross-Validated Models 11.10 K-Fold Cross-Validation 11.11 When Feature Learning Fails 11.12 Conclusion 11.13 Exercises 12 Kernel Methods 12.1 Introduction 12.2 Fixed-Shape Universal Approximators 12.3 The Kernel Trick 12.4 Kernels as Measures of Similarity 12.5 Optimization of Kernelized Models 12.6 Cross-Validating Kernelized Learners 12.7 Conclusion 12.8 Exercises 13 Fully Connected Neural Networks 13.1 Introduction 13.2 Fully Connected Neural Networks 13.3 Activation Functions 13.4 The Backpropagation Algorithm 13.5 Optimization of Neural Network Models 13.6 Batch Normalization 13.7 Cross-Validation via Early Stopping 13.8 Conclusion 13.9 Exercises 14 Tree-Based Learners 14.1 Introduction 14.2 From Stumps to Deep Trees 14.3 Regression Trees 14.4 Classification Trees 14.5 Gradient Boosting 14.6 Random Forests 14.7 Cross-Validation Techniques for Recursively Defined Trees 14.8 Conclusion 14.9 Exercises Part IV Appendices Appendix A Advanced First- and Second-Order Optimization Methods A.1 Introduction A.2 Momentum-Accelerated Gradient Descent A.3 Normalized Gradient Descent A.4 Advanced Gradient-Based Methods A.5 Mini-Batch Optimization A.6 Conservative Steplength Rules A.7 Newton’s Method, Regularization, and Nonconvex Functions A.8 Hessian-Free Methods Appendix B Derivatives and Automatic Differentiation B.1 Introduction B.2 The Derivative B.3 Derivative Rules for Elementary Functions and Operations B.4 The Gradient B.5 The Computation Graph B.6 The Forward Mode of Automatic Differentiation B.7 The Reverse Mode of Automatic Differentiation B.8 Higher-Order Derivatives B.9 Taylor Series B.10 Using the autograd Library Appendix C Linear Algebra C.1 Introduction C.2 Vectors and Vector Operations C.3 Matrices and Matrix Operations C.4 Eigenvalues and Eigenvectors C.5 Vector and Matrix Norms References Index
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