Learn domain adaptation techniques in this 55-minute lecture that explores methods for transferring machine learning models across different domains and datasets. Discover how to address the challenge of applying models trained on one domain to perform effectively on related but distinct domains, examining theoretical foundations and practical approaches for bridging domain gaps. Explore various adaptation strategies, including feature-based methods, instance weighting techniques, and model-based approaches that enable robust performance when training and test data come from different distributions. Gain insights into evaluation methodologies for domain adaptation systems and understand when and how to apply these techniques in real-world natural language processing and speech processing applications.