Domain adaptation is a subfield of machine learning and artificial intelligence that focuses on transferring knowledge from a source domain to a target domain. In other words, it involves adapting a model trained on one dataset (source domain) to perform well on a different but related dataset (target domain). This is particularly useful when there is a mismatch between the distribution of data in the source and target domains, which can lead to decreased performance of the model. Domain adaptation techniques aim to address this issue by incorporating domain knowledge or features into the learning process, allowing the model to generalize better to the target domain. Some common approaches to domain adaptation include transfer learning, adversarial training, and domain-invariant representations. Overall, domain adaptation is a powerful tool for improving the performance of machine learning models in real-world scenarios where data distributions may vary. It is a growing area of research with applications in various fields such as computer vision, natural language processing, and healthcare.