Transfer learning deals with how machine learning and artificial intelligence systems can quickly adapt to new tasks and environments. This in-depth tutorial for students, researchers, and developers covers foundations, plus applications such as text mining, inference on social networks, recommendation, multimedia, and cyber-physical systems.

Qiang Yang is the Head of AI at WeBank and a Chair Professor of Computer Science and Engineering at Hong Kong University of Science and Technology. He is a fellow of the Association for Computing Machinery (ACM), Association for the Advancement of Artificial Intelligence (AAAI), Institute of Electrical and Electronics Engineers (IEEE), International Association for Pattern Recognition (IAPR) and American Association for the Advancement of Science (AAAS), and has served on the AAAI Executive Council and as President of IJCAI. Awards include the 2004/2005 ACM KDDCUP Championship, the ACM SIGKDD Distinguished Service Award, and AAAI Innovative AI Applications Award. His books include Intelligent Planning (1997), Crafting Your Research Future (2012) and Constraint-based Design Recovery for Software Engineering (1997), and he is Founding EIC of the IEEE Transactions on Intelligent Systems and Technology and on Big Data.

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.