'This book offers a comprehensive overview of the field, arguing the case for adaptation as key to mimicking human intelligence … The book includes a substantial bibliography documenting copious citations to the literature. There appear to be few other textbooks in this field apart from this unique work. As such, it will be welcomed by libraries supporting strong computer science programs that may have need for a core text in artificial intelligence.' D. Z. Spicer, Choice
1. Introduction; 2. Instance-based transfer learning; 3. Feature-based transfer learning; 4. Model-based transfer learning; 5. Relation-based transfer learning; 6. Heterogeneous transfer learning; 7. Adversarial transfer learning; 8. Transfer learning in reinforcement learning; 9 Multi-task learning; 10. Transfer learning theory; 11. Transitive transfer learning; 12. AutoTL: learning to transfer automatically; 13. Few-shot learning; 14. Lifelong machine learning; 15. Privacy-preserving transfer learning; 16. Transfer learning in computer vision; 17. Transfer learning in natural language processing; 18. Transfer learning in dialogue systems; 19. Transfer learning in recommender systems; 20. Transfer learning in bioinformatics; 21. Transfer learning in activity recognition; 22. Transfer learning in urban computing; 23. Concluding remarks.
