Course home

by @stats385 · 10 nodes · curated

Hub for Stanford STATS 385 — Analyses of Deep Learning. Official site, instructors, and prior offering.

Bishop Auditorium (meeting place) campus-map.stanford.edu · Sep 24 campus-map.stanford.edu · Sep 24

Wed 3:00–4:20 PM meeting location for the 2019 offering — included for syllabus-style completeness.

STATS 385 on Twitter/X twitter.com · Sep 24 twitter.com · Sep 24

Course announcements channel used during the offering.

Cheat sheet stats385.github.io · Sep 24 stats385.github.io · Sep 24

Course ‘stuff everyone needs to know’ — architectures, datasets, learning motifs at a glance.

Lecture slides stats385.github.io · Sep 24 stats385.github.io · Sep 24

Slides index on the course site. Pair with the lecture-video lists for a full week’s prep.

Reading list (on site) stats385.github.io · Sep 24 stats385.github.io · Sep 24

Canonical reading list page — papers organized by lecture; mirror of what we curate thematically here.

Yiqiao Zhong web.stanford.edu · Sep 24 web.stanford.edu · Sep 24

Course contact. Research on high-dimensional statistics and deep learning theory.

Vardan Papyan statistics.stanford.edu · Sep 24 statistics.stanford.edu · Sep 24

Course organizer / lecturer. Work on Hessians, Neural Collapse, and geometric analyses of deep nets.

David Donoho (Stanford) profiles.stanford.edu · Sep 24 profiles.stanford.edu · Sep 24

Course instructor. Statistics & data science; foundational work on sparse recovery and high-dimensional inference.

STATS 385 (2017 archive) stats385.github.io · Sep 24 stats385.github.io · Sep 24

Previous offering — useful to show how a course site evolves across years while keeping core themes.

STATS 385 course site stats385.github.io · Sep 24 stats385.github.io · Sep 24

Official Fall 2019 course hub: guest lectures, readings, slides, and resources for Analyses of Deep Learning.