TECH 17 — Demystifying Machine Learning and Generative AI
Quarter: Winter
Instructor(s): Gaurav Khanna
Date(s): Jan 22—Mar 12
Class Recording Available: Yes
Class Meeting Day: Thursdays
Grade Restriction: No letter grade
Class Meeting Time: 7:00—8:50 pm (PT)
Tuition: $515
Refund Deadline: Jan 24
Unit(s): 1
Enrollment Limit: 65
Status: Open
Quarter: Winter
Day: Thursdays
Duration: 8 weeks
Time: 7:00—8:50 pm (PT)
Date(s): Jan 22—Mar 12
Unit(s): 1
Tuition: $515
Refund Deadline: Jan 24
Instructor(s): Gaurav Khanna
Grade Restriction: No letter grade
Enrollment Limit: 65
Recording Available: Yes
Status: Open
Artificial intelligence is filled with technical terms like “parameters,” “backpropagation,” and “embedding vectors”—words that can seem like something 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 introduction to machine learning and AI algorithms and systems. 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, the foundations of today’s generative AI platforms such as ChatGPT. We will also examine emerging areas such as agentic AI and AI safety, providing a framework to understand where these technologies may be headed.
Students will come away ready to meaningfully collaborate with AI practitioners, evaluate the evolving AI landscape, and gain context for career exploration.
This course offers a comprehensive introduction to machine learning and AI algorithms and systems. 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, the foundations of today’s generative AI platforms such as ChatGPT. We will also examine emerging areas such as agentic AI and AI safety, providing a framework to understand where these technologies may be headed.
Students will come away ready 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 relies on the use of an external, third-party tool that is not managed or supported by Stanford. Students must purchase their own tool subscriptions and can expect to spend $25-$100 per month. Please see the course syllabus for more details.
GAURAV KHANNA
AI Executive, Cisco Systems
Gaurav Khanna has 25 years of experience in technology and entrepreneurship. He has led efforts to automate business workflows using machine learning and deep learning techniques. His work has focused on using LLMs and generative AI to transform how users interact with sales acceleration platforms. He works with customers to shape their overall AI strategy and drive business transformation and is actively fostering strategic partnerships within Cisco and the broader industry. Khanna received a PhD in materials science and engineering from Stanford. Textbooks for this course:
There are no required textbooks; however, some fee-based online readings may be assigned.