Current and previous announcements:
Thursday, September 26
Tuesday, September 24
- HW #2: Problems 5.7, 5.9, 5.12, 5.14 (Bonus: prove RIP holds for matrices with i.i.d. sub-Gaussian entries.)
- New material posted in classesV2
(sahand dot negahban at yale dot edu)
Office: 24 Hillhouse Rm. 207
Office hours: Wednesday, 1:15-2:30 (Location at 24 Hillhouse Rm. 207)
In this course we will review the recent
advances in high-dimensional statistics. We will cover concepts in
empirical process theory, concentration of measure, and random matrix
theory in the context of understanding the statistical properties of
high-dimensional estimation methods. In this discussion we will also
overview the computational constraints that are involved with solving
high-dimensional problems and touch upon concepts in convex
optimization and online learning.
Lectures: 24 Hillhouse Ave Rm 107; Tues, Thurs 12:00-1:15.
The prerequisites are previous coursework in linear algebra,
multivariate calculus, and basic probability and statistics.
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Last modified: Fri August, 30