ENGR 499

ENGINEERING THE

AI TECHNOLOGY

STACK

General Information

Where: Folsom Lake College Main Campus

When: Fall semester Tuesdays Evenings 6pm to 6:50pm, full semesters (17 meetings) beginning 25 August 2026.

Units: 1

Modality: In-Person with real live actual humans

Instructor: Dan Ross

TL;DR Description

Learn how artificial intelligence is engineered—from circuits to massive data centers—while using AI itself to help teach the course. Hands-on, project-based, and unlike anything else offered.

More Detailed Description

Most students use AI to get answers. In this course, you’ll use AI to build the questions—and the class itself.

ENGR 499 is a one-of-a-kind experimental course at Folsom Lake College where ChatGPT becomes part of the teaching process. Each session, students collaborate with Professor Ross to generate lectures, assignments, and technical explanations in real time—then challenge, refine, and verify everything using engineering principles.

At the same time, you’ll explore the real machinery behind AI: processors, GPUs, memory systems, networking, and massive data centers. You’ll see how intelligence is manufactured—not abstractly, but physically and computationally.

This is not a passive class. It’s hands-on, discussion-driven, and constantly evolving. You’ll work in teams, design systems, and develop the ability to critically evaluate AI-generated content—one of the most valuable skills in today’s world.

If you’re curious, ambitious, and ready to try something different, this course is for you.

Still Reading?... Topics List…

Overview and historical evolution of the AI technology stack.

Digital logic fundamentals; transistors and Boolean operations.

Classic Von Neumann architecture vs. parallel and massively parallel architectures.

GPU, TPU, and custom accelerator design principles.

Dataflow management, memory hierarchy, and bandwidth limitations.

Matrix algebra, tensor operations, and transformer architectures.

Reinforcement learning and neural network training/inference requirements.

Datacenter-scale networking, storage, and I/O systems.

Power delivery, cooling technologies, and thermal modeling.

Reliability, maintainability, and sustainability in AI facilities.

Ethical and environmental considerations in AI infrastructure.

Integration and trade-off analysis across hardware, software, and facilities.