Engineering Mathematics and Artificial Intelligence (eBook, ePUB)
Foundations, Methods, and Applications
Redaktion: Kunze, Herb; Galán, Manuel Ruiz; Riccoboni, Adam; La Torre, Davide
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Engineering Mathematics and Artificial Intelligence (eBook, ePUB)
Foundations, Methods, and Applications
Redaktion: Kunze, Herb; Galán, Manuel Ruiz; Riccoboni, Adam; La Torre, Davide
- Format: ePub
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The fields of Artificial Intelligence and Machine Learning have grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between Mathematics and AI/ML and provides an overview of the current research streams.
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- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 529
- Erscheinungstermin: 26. Juli 2023
- Englisch
- ISBN-13: 9781000907896
- Artikelnr.: 68291406
- Verlag: Taylor & Francis
- Seitenzahl: 529
- Erscheinungstermin: 26. Juli 2023
- Englisch
- ISBN-13: 9781000907896
- Artikelnr.: 68291406
Decision Tree for Classification and Forecasting. 4. A Review of Choice
Topics in Quantum Computing and Some Connections with Machine Learning. 5.
Sparse Models for Machine Learning. 6. Interpretability in Machine
Learning. 7. Big Data: Concepts, Techniques, and Considerations. 8. A
Machine of Many Faces: On the Issue of Interface in Artificial Intelligence
and Tools from User Experience. 9. Artificial Intelligence Technologies and
Platforms. 10. Artificial Neural Networks. 11. Multicriteria Optimization
in Deep Learning. 12. Natural Language Processing: Current Methods and
Challenges. 13. AI and Imaging in Remote Sensing. 14. AI in Agriculture.
15. AI and Cancer Imaging. 16. AI in Ecommerce: From Amazon and TikTok,
GPT-3 and LaMDA, to the Metaverse and Beyond. 17. The Difficulties of
Clinical NLP. 18. Inclusive Green Growth in OECD Countries: Insight from
The Lasso Regularization and Inferential Techniques. 19. Quality Assessment
of Medical Images. 20. Securing Machine Learning Models: Notions and Open
Issues.
Decision Tree for Classification and Forecasting. 4. A Review of Choice
Topics in Quantum Computing and Some Connections with Machine Learning. 5.
Sparse Models for Machine Learning. 6. Interpretability in Machine
Learning. 7. Big Data: Concepts, Techniques, and Considerations. 8. A
Machine of Many Faces: On the Issue of Interface in Artificial Intelligence
and Tools from User Experience. 9. Artificial Intelligence Technologies and
Platforms. 10. Artificial Neural Networks. 11. Multicriteria Optimization
in Deep Learning. 12. Natural Language Processing: Current Methods and
Challenges. 13. AI and Imaging in Remote Sensing. 14. AI in Agriculture.
15. AI and Cancer Imaging. 16. AI in Ecommerce: From Amazon and TikTok,
GPT-3 and LaMDA, to the Metaverse and Beyond. 17. The Difficulties of
Clinical NLP. 18. Inclusive Green Growth in OECD Countries: Insight from
The Lasso Regularization and Inferential Techniques. 19. Quality Assessment
of Medical Images. 20. Securing Machine Learning Models: Notions and Open
Issues.