TECH 152 Z — A Crash Course in Artificial Intelligence (AI): Part I
Quarter: Winter
Instructor(s): Ronjon Nag
Date(s): Jan 14—Feb 11
Class Recording Available: Yes
Class Meeting Day: Tuesdays
Class Meeting Time: 6:30—8:30 pm (PT)
Tuition: $410
Refund Deadline: Jan 16
Unit(s): 1
Status: Registration opens Dec 2, 8:30 am (PT)
Quarter: Winter
Day: Tuesdays
Duration: 5 weeks
Time: 6:30—8:30 pm (PT)
Date(s): Jan 14—Feb 11
Unit(s): 1
Tuition: $410
Refund Deadline: Jan 16
Instructor(s): Ronjon Nag
Recording Available: Yes
Status: Registration opens Dec 2, 8:30 am (PT)
Artificial intelligence is in the news daily. This course will provide a high-level overview of AI techniques. Through pre-built, hands-on exercises, we will discuss how current AI platforms compare with how the brain works and how AI systems actually “learn.” Specifically, we will cover neural networks and their applicability to generative AI and large language models. We will also discuss the societal and ethical issues surrounding the real-world applications of AI. By the end of the course, students will understand how AI techniques work so they can (1) converse with AI 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 AI 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.
This is the first course in a two-part sequence. Students may enroll in “A Crash Course in Artificial Intelligence (AI): Part I” without intending to enroll in “A Crash Course in Artificial Intelligence (AI): Part II.” This course introduces foundational machine learning principles. While previous experience isn't required to take the course, those with a STEM background may have less difficulty with its technical concepts. Students can choose to attend this course on campus or online. Sign up for Section H if you think you might attend class on the Stanford campus at least once. There is no commitment—you can still choose to attend via Zoom for any session. Sign up for Section Z if you know you will exclusively attend via Zoom.
RONJON NAG
Adjunct Professor in Genetics, Stanford Medicine
Ronjon Nag has been building AI systems for 40 years and co-founded or advised companies sold to Motorola, RIM/BlackBerry, and Apple. He is a venture capitalist and president of the R42 Group, which invests in and creates AI and longevity companies. He teaches AI, genes, ethics, and venture capital at Stanford Medicine and is a visiting fellow at the Stanford Center for the Study of Language and Information. He became a Stanford Interdisciplinary Distinguished Careers Institute Fellow in 2016. He received a PhD from Cambridge, an MS from MIT, and a BSc from Birmingham, United Kingdom. He has also received the MIT Great Dome Award, the IET Mountbatten Medal, the $1 million Verizon Powerful Answers Award, the 2021 IEEE-SCV Outstanding Engineer Award, and the 2023 CogX AI Lifetime Achievement Award and is the 2024 inductee in the Silicon Valley Engineering Hall of Fame. He is part owner of some 100 AI and biotech startups. Textbooks for this course:
(Required) Tariq Rashid, Make a Neural Network (ISBN 978-1530826605)