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TECH 152 — A Crash Course in AI

Quarter: Summer
Day(s): Wednesdays
Course Format: Live Online (About Formats)
Duration: 4 weeks
Date(s): Aug 2—Aug 23
Time: 7:00—9:00 pm (PT)
Refund Deadline: Aug 4
Unit: 0
Grade Restriction: NGR only; no credit/letter grade
Tuition: $340
Instructor(s): Ronjon Nag
Class Recording Available: Yes
Status: Open
DOWNLOAD THE SYLLABUS » (subject to change)
Live Online(About Formats)
7:00—9:00 pm (PT)
Aug 2—Aug 23
4 weeks
Refund Date
Aug 4
0 Unit
Grade Restriction
NGR only; no credit/letter grade
Ronjon Nag
DOWNLOAD THE SYLLABUS » (subject to change)
Artificial intelligence (AI), inspired by our understanding of how the human brain learns and processes information, has given rise to powerful techniques known as neural networks and deep learning. This course will provide a high-level overview of these and other AI techniques. Through pre-built hands-on exercises, we will discuss how current AI platforms compare with how the brain works, how systems actually “learn,” and how to build and apply neural networks. We will also discuss the societal and ethical issues surrounding the real-world applications of neural networks. By the end of the course, students will understand how AI techniques work so they can (1) converse with neural network practitioners and companies; (2) be able to critically evaluate AI news stories and technologies; and (3) consider what the future of AI can hold and what barriers need to be overcome with current neural network models. This course is ideal for product managers who interact with data scientists, software engineers who wish for more AI exposure, and anyone in the general public who wants to know how current AI works.

No computer science or programming experience is needed, but an understanding of simple algebra is expected.

Adjunct Professor in Genetics, Stanford School of Medicine; Visiting Fellow, Stanford Center for the Study of Language and Information; President, R42 Group

Ronjon Nag has invented AI systems for three decades. He has started companies sold to Motorola, BlackBerry, and Apple. In 2016, he became a Stanford Interdisciplinary Distinguished Careers Institute Fellow. Nag received a PhD in engineering from Cambridge. He also received the IET Mountbatten Medal, the $1M Verizon Prize for Bounce Imaging, and the 2021 IEEE-SCV Outstanding Engineer Award.

Textbooks for this course:

(Required) Tariq Rashid , Make Your Own Neural Network, 1st Edition (ISBN 978-1530826605)