1a) We will read the following book.
1b) Also see the review article .
1c) Also see
interesting projects from Stanford.
2) We will read a few related papers. Understand how Deep Learning may
be related to other topics in Data Sciences... Particular Emphasis:
AdTech (DMP and Attribution), FinTech (compare to Kensho, Palantir,
Sentient), Verifier-Recommenders (Cyber Security, Privacy, AI to
obfuscate AI), Caner, Linguistics, ...
3) Each participant will implement a deep learning application with
open source code (e.g., caffe or alexnet). In preparation for this we
will read the
following paper.
4) Jointly with MIT, Cornell and UW, we have submitted a grant proposal to
NSF to understand interconnections among Manifold Learning, Deep
Learning and PH-Learning (under review).
5) For cancer applications, we have been funded by NCI to start
a center (focus on
causality & topology). Also we are organizing a summer school.
Fequently Asked Questions (FAQs)
Q1. Can I attend this class?
A1. Yes, but... only if you have been invited.
Q2. Can I get a grade/credit?
A2. No, this is an informal class. But we will write paper(s), which
will show how DEEP-ly we understand these topics. We can create some
programming projects, which can be evaluated and displayed on a
leader-board. We can also take up some problems in Kaggle and use
their evaluations to demonstrate depth of our understanding.
Any other ideas?