HighDimensional Probability and StatisticsSebastien Roch, Department of Mathematics, UWMadison
This course provides a rigorous, selfcontained introduction to the area of highdimensional probability and statistics from a nonasymptotic perspective, aimed at graduate students in mathematics, statistics, computer science and engineering. It will include a focus on core methodology and theory (tail bounds, concentration of measure, random matrices) as well as indepth exploration of particular model classes (sparse linear models, graphical models, community detection). No statistics background will be assumed. Prior exposure to graduatelevel probability (e.g., MATH 733 or ECE 730 or STAT 709) is highly recommended. Course Information
Course number: MATH/STAT/ECE 888  Topics in Mathematical Data Science ArchiveWebsites from previous semesters: Lecture NotesLecture notes are available here. Relevant code can be found here. TextbooksTopics to be covered are taken partly from the following textbooks (but see also the full list of references):
