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TECH 152 — A Crash Course in Artificial Intelligence

Quarter: Winter
Day(s): Mondays
Course Format: Live Online (About Formats)
Duration: 4 weeks
Date(s): Jan 23—Feb 13
Time: 7:00—9:00 pm (PT)
Refund Deadline: Jan 25
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)
Jan 23—Feb 13
4 weeks
Refund Date
Jan 25
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 artificial intelligence 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 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 (ISBN 978-1530826605)