WSP 152 — A Crash Course in Artificial Intelligence
Course Format: On-campus
Duration: 1 day
Date(s): Jun 29
Time: 9:00 am—5:00 pm
Drop Deadline: Jun 22
Grade Restriction: NGR only; no credit/letter grade
Instructor(s): Ronjon Nag
9:00 am—5:00 pm
NGR only; no credit/letter grade
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 workshop 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 one-day crash 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.
Ronjon Nag, Fellow, Stanford Distinguished Careers Institute; Fellow, Stanford Center for the Study of Language and InformationRonjon Nag has deployed artificial intelligence systems over three decades. He received a PhD in engineering from Cambridge, an MS from MIT, and the Mountbatten Medal from the Royal Institution of Engineering and Technology. Companies he has co-founded or advised have been sold to Motorola, BlackBerry, and Apple.
Textbooks for this course:
(Recommended) Tariq Rashid, Make Your Own Neural Network (ISBN 978-1530826605)