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SCI 57 — Artificial Intelligence for Healthcare and Longevity

Quarter: Spring
Day(s): Tuesdays
Course Format: On-campus
Duration: 6 weeks
Date(s): Apr 23—May 28
Time: 7:00—8:50 pm
Drop Deadline: May 6
Unit: 1
Tuition: $375
Instructor(s): Ronjon Nag
Status: Registration opens Feb 25, 8:30 am (PT)
Spring
On-campus
Tuesdays
7:00—8:50 pm
Date(s)
Apr 23—May 28
6 weeks
Drop By
May 6
1 Unit
Fees
$375
Instructor(s):
Ronjon Nag
Registration opens Feb 25, 8:30 am (PT)
We are at an inflection point in applying artificial intelligence (AI) to the fields of healthcare and longevity due to the rapid cost reductions of genome sequencing, the patient-centered data explosion caused by wearables such as fitness trackers and heart monitors, and the increased availability of medical and drug databases. This course provides an introductory overview of the intersection of AI, healthcare, and longevity. We will begin by discussing how today’s AI functions compare to how the human brain works, and how to build and actually apply deep learning and neural networks—a form of AI—with prebuilt hands-on exercises. Then we will focus on AI applications in medicine for diagnosis and drug discovery, with the biology overview necessary to understand the implications. Finally, we will discuss non-medical AI applications and societal issues relevant to the aging society—for example, robot companions and financial, population, and ethical issues. By the end of the course, students will understand how AI techniques work so they can converse with AI practitioners and companies, specifically those targeting wellness and aging; critically evaluate AI news stories and technologies; and consider the implications of AI on an aging society.

This is an introductory course. No computer science, biology, or programming experience is needed, but an understanding of middle-school math (e.g., simple algebra) is expected.

Ronjon Nag, Interdisciplinary Fellow, Stanford Distinguished Careers Institute

Ronjon Nag has deployed artificial intelligence systems over three decades. He received a PhD in engineering from Cambridge and an MS from MIT, and at Stanford he was a Harkness Fellow. He also received the Mountbatten Medal from the Institution of Engineering and Technology. Companies he has co-founded or advised have been sold to Motorola, BlackBerry, and Apple. He is an advisor to several AI-oriented biotech companies including Healx, a machine learning drug discovery company.

Textbooks for this course:

(Recommended) Tariq Rashid, Make your own Neural Network (ISBN 978-1530826605)
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)