Learning
Paths
Each course is a complete 18-week experience: a central project arc, three entry tracks for students at different levels, portfolio-based assessment, and a public exhibition at semester end. No exams. No required textbook purchases. All course materials free and open access.
Course Sequence
CS pathway flows left to right. Engineering track branches at Math 2B / ENGR 11. All courses accessible at Track I with no prior programming.
“Tracks are chosen weekly, not at semester start. A student can run Track I for ten weeks and switch to Track II for the capstone. There is no grade penalty for choosing Track I.”
The Key Design Insight — Track SystemEvery course in this curriculum is designed to run at three simultaneous depth levels. These are not ability groups, remediation tiers, or ceiling categories — they are depth choices. The same concept is offered to everyone. What varies is whether a student builds it, proves it, or extends it. Track I is a complete, serious course outcome — not a consolation.
Novice — Build & Understand
Students develop genuine working fluency with the core concept. They build a complete, functional implementation and can explain what it does and why. No prior experience required at Track I — by design.
Builder — Implement & Extend
Students implement the concept rigorously, handle edge cases, and extend it to a novel context. They move between their own implementation and a library version and explain the tradeoffs in writing.
Architect — Prove & Research
Students engage with the formal mathematical structure of the concept, read related research, and contribute something original — a proof, an optimization, a novel application, or a written critical analysis.
Six Courses
Filter by track level, or view all courses in the curriculum. Each course card links to its full detail page.
PageRank. GPS. The phone in your pocket. Three technologies derived from first principles. Includes the signature project: Build a Computer from Scratch.
AI is not magic — it is math, history, and human choice. We build from first principles: probability, search, neural networks, language models.
ML algorithms are not neutral mathematical facts — they are choices about what to optimize, whose data counts, who bears the error.
Every data structure is an argument about the world. We implement every structure before using the library version. No LeetCode grind — deep projects that transfer.
Six fundamental problems. Twenty-nine lessons. Build circuits, collect data, verify models. Derive before compute — the ALAF approach.
Model real circuits. Understand why 0.1 + 0.2 ≠ 0.3. MATLAB the way engineers use it — from binary representation up to IEEE 754.