Statistical And Computational Foundations of Modern AI

6 units · Graduate

All Semesters: 6 units This course studies the statistical and computational foundations of modern AI and data science. It connects core ideas from statistics with current methods for learning, prediction, and inference. Topics include optimization, representation learning, and methods for fitting and sampling from complex models. The course emphasizes how algorithmic choices affect statistical accuracy and computational efficiency. Mathematical ideas are developed alongside computational examples and applications. It is intended for master's and PhD students in statistics and related fields.