Machine Learning for Engineers
(Sprache: Englisch)
All stars are known to power strong stellar winds at the end of their lives, expelling stellar material that is recycled as building blocks of new planets and life. IAU S366 provides an overview of state-of-the-art observational, theoretical and numerical...
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Klappentext zu „Machine Learning for Engineers “
All stars are known to power strong stellar winds at the end of their lives, expelling stellar material that is recycled as building blocks of new planets and life. IAU S366 provides an overview of state-of-the-art observational, theoretical and numerical studies on the origin of winds in evolved stars.
Inhaltsverzeichnis zu „Machine Learning for Engineers “
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.
Autoren-Porträt von Simeone Osvaldo
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.
Bibliographische Angaben
- Autor: Simeone Osvaldo
- 2022, New ed, XXII, 578 Seiten, Maße: 20,8 x 26 cm, Gebunden, Englisch
- Verlag: Cambridge University Pr.
- ISBN-10: 1316512827
- ISBN-13: 9781316512821
Sprache:
Englisch
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