STAT 317 - Computational Optimal Transport Learning Course

[STAT 317 - Computational Optimal Transport Learning Course] STAT 317 - Computational Optimal Transport STAT 317 explores the theory and applications of optimal transport theory in computational statistics and data science. The course covers fundamental concepts in optimal transport, including Kantorovich formulation, Wasserstein distances, and applications in data analysis, image processing, and machine learning. Emphasis is placed on understanding the geometric and probabilistic interpretations of optimal transport, numerical algorithms for solving optimal transport problems, and applications in statistical inference, domain adaptation, and generative modeling. Through theoretical lectures and hands-on projects, students gain proficiency in implementing optimal transport algorithms, analyzing high-dimensional data distributions, and leveraging optimal transport theory to solve real-world problems in statistics and data-driven decision-making. The course equips students with advanced computational skills, mathematical reasoning, and analytical tools necessary for addressing complex challenges in modern data science and computational statistics.This Learning Course is a platform that helps students to study and learn. The platform provides all the required material for the students to study. The material includes Study guide, Exam Questions & Answers, Study Notes and other resources that are useful for the students. It also gives information about the upcoming exams so that students can prepare themselves accordingly.