CaltechAUTHORS
  A Caltech Library Service

ACM 217: Probability in High Dimensions

Tropp, Joel A. (2021) ACM 217: Probability in High Dimensions. [Teaching Resource] (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220412-221302767

[img] PDF - Accepted Version
See Usage Policy.

4MB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20220412-221302767

Abstract

ACM 217 is a second-year graduate course on high-dimensional probability, designed for students in computing and mathematical sciences. We discuss phenomena that emerge from probability models with many degrees of freedom, tools for working with these models, and a selection of applications to computational mathematics. The Winter 2021 edition of ACM 217 is the fourth instantiation of a class that initially focused on concentration inequalities and that has expanded to include other topics in high-dimensional probability. This year, the course was more mathematical than some previous editions, with less attention to tools and applications. This slant may not serve applied students well, and it is likely that future versions of the course will strike a different balance between theory and practice. These lecture notes document ACM 217 as it was taught in Winter 2021. The notes are being transcribed by the students as part of their coursework, and they are edited lightly by the instructor. They are intended as a record for the students who have taken the course. Other readers should beware that this course is neither refined nor especially coherent. There is no warranty about correctness. Furthermore, these notes have been prepared using many sources and without appropriate scholarly citations.


Item Type:Teaching Resource
ORCID:
AuthorORCID
Tropp, Joel A.0000-0003-1024-1791
Additional Information:© 2021 Joel A. Tropp. Typeset on April 14, 2022. These notes have been transcribed from the lectures by the participants in the course: Chi-Fang Chen, Yifan Chen, Anushri Dixit, Ethan Epperly, Hamed Hamze, Hsin-Yuan Huang, Taylan Kargin, Eitan Levin, Jack Li, Serena Liu, Riley Murray, Nicholas H. Nelson, Joe Slote, Roy Wang, Jing Yu, Kevin Yu, Ziyun Zhang. Many thanks are due for their care and diligence. All remaining errors are the fault of the instructor.
Group:Caltech CMS Lecture Notes
Series Name:Caltech CMS Lecture Notes
Issue or Number:2021-01
DOI:10.7907/mxr0-c422
Record Number:CaltechAUTHORS:20220412-221302767
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220412-221302767
Official Citation:Joel A. Tropp, ACM 217: Probability in High Dimensions, [Caltech CMS Lecture Notes 2021-01], Caltech, Pasadena, Winter 2021. https://resolver.caltech.edu/CaltechAUTHORS:20220412-221302767
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114267
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:13 Apr 2022 21:40
Last Modified:18 Apr 2022 18:11

Repository Staff Only: item control page