TECH 17 — Demystifying Machine Learning and Generative AI
Quarter: Winter
Instructor(s): Gaurav Khanna
Date(s): Jan 21—Mar 11
Class Recording Available: Yes
Class Meeting Day: Thursdays
Grade Restriction: No letter grade
Class Meeting Time: 7:00—8:50 pm (PT)
Tuition: $650
Refund Deadline: Jan 23
Unit(s): 1
Status: Registration opens Nov 30, 8:30 am (PT)
Artificial intelligence is filled with technical terms like “parameters,” “cost functions,” and “context window.” These words can seem like things only a machine would understand. Yet AI can be reduced to a handful of foundational principles and concepts that anyone can grasp. This course offers a comprehensive, non-technical introduction to machine learning, deep learning, and generative AI. Through lectures, interactive discussions, and real-world examples, students will explore the mechanics of AI systems without advanced math or coding. The course begins with regression algorithms—models that capture relationships between variables—and builds toward neural networks and deep learning, then concludes with the foundations of today’s generative AI platforms such as ChatGPT and Claude. We will also examine exciting new areas such as agentic AI and AI safety, providing a framework to understand where these technologies may be headed. By the end, students will be able to meaningfully collaborate with AI practitioners, evaluate the evolving AI landscape, and gain context for career exploration.
Knowledge of algebra is recommended; no coding experience is required. This course will demonstrate third-party AI tools that are not managed or supported by Stanford. Students can explore these tools on their own if they like, but use is optional. Many tools offer free versions or trials; paid subscriptions, if chosen, typically range from $25–$100 per month. See the syllabus for more details.