Machine Learning Refined: Foundations, Algorithms, and Applications 2nd Edition, ISBN-13: 978-1108480727

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Machine Learning Refined: Foundations, Algorithms, and Applications 2nd Edition, ISBN-13: 978-1108480727

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• Publisher: ‎ Cambridge University Press; 2nd edition (March 12, 2020)
• Language: ‎ English
• 594 pages
• ISBN-10: ‎ 1108480721
• ISBN-13: ‎ 978-1108480727

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.

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.

Table of Contents:

Half-title

Title page

Copyright information

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

Jeremy Watt received his Ph.D. in Electrical Engineering from Northwestern University, Illinois, and is now a machine learning consultant and educator. He teaches machine learning, deep learning, mathematical optimization, and reinforcement learning at Northwestern University, Illinois.

Reza Borhani received his Ph.D. in Electrical Engineering from Northwestern University, Illinois, and is now a machine learning consultant and educator. He teaches a variety of courses in machine learning and deep learning at Northwestern University, Illinois.

Aggelos K. Katsaggelos is the Joseph Cummings Professor at Northwestern University, Illinois, where he heads the Image and Video Processing Laboratory. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), SPIE, the European Association for Signal Processing (EURASIP), and The Optical Society (OSA) and the recipient of the IEEE Third Millennium Medal (2000).

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