Explore the intersection of statistics and computer science in machine learning through this 46-minute lecture by Madeleine Udell from Cornell University. Delve into the concept of low-rank structures in big data, covering topics such as principal components analysis, generalized low-rank models, and their applications in various fields. Learn about fitting GLRMs using alternating minimization, imputing missing and heterogeneous data, and validating models. Discover how low-rank models can be applied to dimensionality reduction, autoML, and experiment design for timely model selection. Examine real-world examples, including hospitalizations and political data tables, to understand the practical implications of low-rank structures. Gain insights into latent variable models and their approximate low-rank nature, bridging the gap between statistical theory and computational efficiency in the era of Big Data.