Information-Driven Machine Learning

TRAMA

This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field. Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing the discipline's robustness and credibility. While bridging the gap between machine learning and disciplines such as physics, information theory, and computer engineering, this textbook maintains an accessible and comprehensive style, making complex topics digestible for a broad readership. Information-Driven Machine Learning explores the synergistic harmony among these disciplines to enhance our understanding of data science modeling. Instead of solely focusing on the "how," this text provides answers to the "why" questions that permeate the field, shedding light on the underlying principles of machine learning processes and their practical implications. By advocating for systematic methodologies grounded in fundamental principles, this book challenges industry practices that have often evolved from ideologic or profit-driven motivations. It addresses a range of topics, including deep learning, data drift, and MLOps, using fundamental principles such as entropy, capacity, and high dimensionality. Ideal for both academia and industry professionals, this textbook serves as a valuable tool for those seeking to deepen their understanding of data science as an engineering discipline. Its thought-provoking content stimulates intellectual curiosity and caters to readers who desire more than just code or ready-made formulas. The text invites readers to explore beyond conventional viewpoints, offering an alternative perspective that promotes a big-picture view for integrating theory with practice. Suitable for upper undergraduate or graduate-level courses, this book can also benefit practicing engineers and scientists in various disciplines by enhancing their understanding of modeling and improving data measurement effectively.

SOMMARIO

Preface 1 Introduction 1.1 Science 1.2 Data Science 1.3 Information Measurements 1.4 Exercises 1.5 Further Reading 2 The Automated Scientific Process 2.1 The Role of the Human 2.1.1 Curiosity 2.1.2 Data Collection 2.1.3 The Data Table 2.2 Automated Model Building 2.2.1 The Finite State Machine 2.2.2 How Machine Learning Generalizes 2.3 Exercises 2.4 Further Reading 3 The (Black Box) Machine Learning Process 3.1 Types of Tasks 3.1.1 Unsupervized Learning 3.1.2 Supervized Learning 3.2 Black Box Machine Learning Process 3.2.1 Training/Validation Split 3.2.2 Independent but Identically Distributed 3.3 Types of Models 3.3.1 Nearest Neighbors 3.3.2 Linear Regression 3.3.3 Decision Trees 3.3.4 Random Forests 3.3.5 Neural Networks 3.3.6 Support Vector Machines 3.3.7 Genetic Programming 3.4 Error Metrics 3.4.1 Binary Classification 3.4.2 Detection 3.4.3 Multi-class Classification 3.4.4 Regression 3.5 The Information-based Machine Learning Process 3.6 Exercises 3.7 Further Reading 4 Information Theory 4.1 Probability, Uncertainty, Information 4.1.1 Chance and Probability 4.1.2 Probability Space 4.1.3 Uncertainty and Entropy 4.1.4 Information 4.2 Minimum Description Length 4.3 Information in Curves 4.4 Information in a Table 4.5 Exercises 4.6 Further Reading 5 Capacity 5.1 Intellectual Capacity 5.1.1 Minsky’s Criticism 5.1.2 Cover’s Solution 5.1.3 MacKay’s Viewpoint 5.2 Memory-equivalent Capacity of a Model 5.3 Exercises 5.4 Further Reading 6 The Mechanics of Generalization 6.1 Logic Definition of Generalization 6.2 Translating a Table into a Finite State Machine 6.3 Generalization as Compression 6.4 Resilience 6.5 Adversarial Examples 6.6 Exercises 6.7 Further Reading 7 Meta-Math: Exploring the Limits of Modeling 7.1 Algebra 7.1.1 Garbage In, Garbage Out 7.1.2 Randomness 7.1.3 Transcendental Numbers 7.2 No Rule without Exception 7.2.1 Compression by Association 7.3 Correlation vs Causality 7.4 No Free Lunch 7.5 All Models are Wrong 7.6 Exercises 7.7 Further Reading 8 Capacity of Neural Networks 8.1 Memory-equivalent Capacity of Neural Networks 8.2 Upper-bounding the MEC Requirement of a Neural Network given Training Data 8.3 Topological Concerns 8.4 MEC for Regression Networks 8.5 Exercises 8.6 Further Reading 9 Neural Network Architectures 9.1 Deep Learning and Convolutional Neural Networks 9.1.1 Convolutional Neural Networks 9.1.2 Residual Networks 9.2 Generative Adversarial Networks 9.3 Autoencoders 9.4 Transformers 9.4.1 Architecture 9.4.2 Self-Attention Mechanism 9.4.3 Positional Encoding 9.4.4 Example Transformation 9.4.5 Applications and Limitations 9.5 The Role of Neural Architectures 9.6 Exercises 9.7 Further Reading 10 Capacities of some other Machine Learning Methods 10.1 k-Nearest Neighbors 10.2 Support Vector Machines 10.3 Decision Trees 10.3.1 Converting a Table into a Decision Tree 10.3.2 Decision Trees 10.3.3 Generalization of Decision Trees 10.3.4 Ensembling 10.4 Genetic Programming 10.5 Unsupervized Methods 10.5.1 k-means Clustering 10.5.2 Hopfield Networks 10.6 Exercises 10.7 Further Reading 11 Data Collection and Preparation 11.1 Data Collection and Annotation 11.2 Task Definition 11.3 Well-Posedness 11.3.1 Chaos and how to avoid it 11.3.2 Forcing Well-Posedness 11.4 Tabularization 11.4.1 Table Data 11.4.2 Time-Series Data 11.4.3 Natural Language and other Varying-Dependency Data 11.4.4 Perceptual Data 11.4.5 Multimodal Data 11.5 Data Validation 11.5.1 Hard Conditions 11.5.2 Soft Conditions 11.6 Numerization 11.7 Imbalanced Data 11.7.1 Extension beyond simple Accuracy 11.8 Exercises 11.9 Further Reading 12 Measuring Data Sufficiency 12.1 Dispelling a Myth 12.2 Capacity Progression 12.3 Equilibrium Machine Learner 12.4 Data Sufficiency Using the Equilibrium Machine Learner 12.5 Exercises 12.6 Further Reading 13 Machine Learning Operations 13.1 What makes a predictor production-ready? 13.2 Quality Assurance for Predictors 13.2.1 Traditional Unit Testing 13.2.2 Synthetic Data Crash Tests 13.2.3 Data Drift Test 13.2.4 Adversarial Examples Test 13.2.5 Regression Tests 13.3 Measuring Model Bias 13.3.1 Where does the bias come from? 13.4 Security and Privacy 13.5 Exercises 13.6 Further Reading 14 Explainability 14.1 Explainable to Whom? 14.2 Occam’s Razor Revisited 14.3 Attribute Ranking: Finding what Matters 14.4 Heatmapping 14.5 Instance-based Explanations 14.6 Rule Extraction 14.6.1 Visualizing Neurons and Layers 14.6.2 Local Interpretable Model-agnostic Explanations (LIME) 14.7 Future Directions 14.7.1 Causal Inference 14.7.2 Interactive Explanations 14.7.3 Explainability Evaluation Metrics 14.8 Fewer Parameters 14.9 Exercises 14.10 Further Reading 15 Repeatability and Reproducibility 15.1 Traditional Software Engineering 15.2 Why Reproducibility Matters 15.3 Reproducibility Standards 15.4 Achieving Reproducibility 15.5 Beyond Reproducibility 15.6 Exercises 15.7 Further Reading 16 The Curse of Training and the Blessing of High Dimensionality 16.1 Training is Difficult 16.1.1 Common Workarounds 16.2 Training in Logarithmic Time 16.3 Building Neural Networks Incrementally 16.4 The Blessing of High Dimensionality 16.5 Exercises 16.6 Further Reading 17 Machine Learning and Society 17.1 Societal Reaction: The Hype Train, Worship, or Fear 17.2 Some Basic Suggestions from a Technical Perspective 208 17.2.1 Understand Technological Diffusion and Allow Society Time to Adapt 17.2.2 Measure Memory-Equivalent Capacity (MEC) 17.2.3 Focus on Smaller, Task-Specific Models 17.2.4 Organic Growth of Large-Scale Models from Small-Scale Models 17.2.5 Measure and Control Generalization to solve Copyright Issues 17.2.6 Leave Decisions to qualified Humans 17.3 Exercises 211 17.4 Further Reading Appendix A Recap: The Logarithm Appendix B More on Complexity Appendix C Concepts Cheat Sheet Appendix D A Review Form that Promotes Reproducibility List of illustrations Bibliography

AUTORE

Gerald Friedland: Listed in the AI2000 Most Influential Scholar list as one of the top-cited research scholars in AI in the last decade, Friedland's contributions to the field of machine learning have been both substantial and enduring since he started working in the field in 2001. His Simple Interactive Object Extraction algorithm has been part of open source image editing and creation tools since 2005 and his cloud-less MOVI Speech Recognition board has been used by makers since 2015.  Currently, he is adjunct faculty at the University of California, Berkeley, a Faculty Fellow of the Berkeley Institute of Data Science, and a Principal Scientist in the Sagemaker team at Amazon AWS. After earning his Ph.D. from Freie Universität Berlin in 2006, Gerald led a team of researchers in speech and multimedia content analysis as the Director of Audio and Multimedia research at the International Computer Science Institute in Berkeley. He then held the role of Principal Data Scientist at Lawrence Livermore National Lab from 2016 to 2019. That year, he co-founded Brainome, Inc., where he harnessed his technical expertise to develop an automatic machine learning tool rooted in the information measurement techniques central to this book. His journey then took him to Amazon AWS in 2022 as a Principal Scientist, AutoML. Beyond his industry and academic roles, Gerald is a seasoned author. His literature contributions span from the textbooks Multimedia Computing (Cambridge University Press) and Multimodal Location Estimation of Videos and Images (Springer) to a programming book for young children published by Apress.

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9783031394768
• Dimensioni: 235 x 155 mm Ø 606 gr
• Formato: Copertina rigida
• Illustration Notes: XXII, 267 p. 50 illus., 33 illus. in color.
• Pagine Arabe: 267
• Pagine Romane: xxii