Explore the concept of equitable data valuation in machine learning through this 44-minute lecture by James Zou from Stanford University. Delve into the importance of measuring data value, focusing on its role in machine learning contexts. Learn about the Leave One Out Method and Data Shapley Value, understanding their desirable properties and applications. Examine real-world case studies, including UK Biobank lung cancer prediction, face recognition domain adaptation, dermatology classification, and clinical notes NLP. Discover how removing low-value data and adding high-value data impacts prediction accuracy, and how negative Shapley values can identify mislabeled data. Gain insights into efficient approximation methods for data Shapley and explore new frontiers in data valuation.