Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering problems. With reproducible examples using Matlab, and lecture slides and solutions for instructors, this is the ideal introduction for engineering students of all disciplines.
Designed with engineers in mind, this self-contained book will equip students with everything they need to apply machine learning principles to real-world engineering problems. With reproducible examples using Matlab, and lecture slides and solutions for instructors, this is the ideal introduction for engineering students of all disciplines.
Osvaldo Simeone is a Professor of Information Engineering at King's College London, where he directs King's Communications, Learning & Information Processing (KCLIP) lab. He is a Fellow of the IET and IEEE.
Inhaltsangabe
Part I. Introduction and Background: 1. When and how to use machine learning; 2. Background. Part II. Fundamental Concepts and Algorithms: 3. Inference, or model-driven prediction; 4. Supervised learning: getting started; 5. Optimization for machine learning; 6. Supervised learning: beyond least squares; 7: Unsupervised learning. Part III. Advanced Tools and Algorithms: 8. Statistical learning theory; 9. Exponential family of distributions; 10. Variational inference and variational expectation maximization; 11. Information-theoretic inference and learning; 12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning: 13. Transfer learning, multi-task learning, continual learning, and meta-learning; 14. Federated learning. Part V. Epilogue: 15. Beyond this book.
Part I. Introduction and Background: 1. When and how to use machine learning; 2. Background. Part II. Fundamental Concepts and Algorithms: 3. Inference, or model-driven prediction; 4. Supervised learning: getting started; 5. Optimization for machine learning; 6. Supervised learning: beyond least squares; 7: Unsupervised learning. Part III. Advanced Tools and Algorithms: 8. Statistical learning theory; 9. Exponential family of distributions; 10. Variational inference and variational expectation maximization; 11. Information-theoretic inference and learning; 12. Bayesian learning. Part IV. Beyond Centralized Single-Task Learning: 13. Transfer learning, multi-task learning, continual learning, and meta-learning; 14. Federated learning. Part V. Epilogue: 15. Beyond this book.
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