Course Catalog & Credential Pathway — Dwg. No. PF-002
A sequence for students headed toward building the models, not checking them — aimed at research and engineering roles inside AI labs and the companies built on top of them.
Companion volume to Dwg. No. PF-001, The Verification Track — same catalog format, opposite target role.
Five strands, running through every phase
No frameworks yet. Two years of straight math and programming fundamentals — the deficit that shows up latest and hurts most is always a weak foundation here, not an unfamiliar library.
Precalculus, Accelerated
Compressed to clear room for calculus by grade 11 — the standard on-ramp for anyone headed toward a quantitative degree.
Programming I — Python
Variables, control flow, functions, data structures. Fluency, not tricks.
Discrete Mathematics & Logic
Sets, proofs, graphs, combinatorics — the math CS theory actually runs on, usually skipped until it's overdue.
Technical Writing for Research
Writing a clear methods section and an honest results section — the two paragraphs every paper lives or dies on.
The math and CS sequences converge into an actual first model. Kept a year behind where most bootcamps start, on purpose — the gap is what lets the model make sense instead of just running.
Calculus I & II
Through multivariable and the gradient — backpropagation is the chain rule with bookkeeping, and it should read that way.
MATH 101
Linear Algebra
Vector spaces, eigendecomposition, matrix calculus. Every tensor operation is this course wearing a framework's syntax.
MATH 101
Probability & Statistics for ML
Distributions, estimation, Bayes — framed toward loss functions and uncertainty, not toward the social-science stats track.
Data Structures & Algorithms
Complexity analysis and the standard structures, drilled to fluency — still the baseline technical-interview bar at every AI lab.
CS 100
Intro to Machine Learning
Regression through a first neural net, built from array operations before any framework is allowed to hide the mechanics.
MATH 210, STAT 220
Research Seminar — Reading Group
Weekly seminal-paper reads (perceptron through transformers), presented and defended aloud, not just summarized.
A CS or applied-math degree is the default format here — unlike the Verification Track, this role's credentialing is concentrated in a small number of institutions and lab-affiliated programs, not spread across trade and licensing bodies.
CS / Applied Math Degree
Formal core: algorithms, systems, theory of computation. The credential and the peer network both matter here.
Deep Learning & a Specialization
Architectures in depth — transformers, diffusion, RL — then one specialization chosen and pushed further than the survey. Eight standalone curricula below, one per field where these same architectures are currently doing the most visible work outside AI itself: computational biology, materials science, climate modeling, fusion energy, mathematics, chip design, robotics, and agriculture.
ML 230
Systems for ML
Distributed training, GPU/accelerator programming, the infrastructure that turns a notebook model into one that trains at scale.
CS 210
Supervised Research Practicum
Lab placement or an open-source ML project with a mentor of record. Graded on a reproduction that actually reproduces, or a contribution that gets merged.
RES 240, ML 310
Capstone — Build & Ship
One model or tool taken from idea to a deployed, load-bearing artifact with real users. The portfolio piece an application actually turns on.
ENG 320, RES 350
Nine fields where the architectures taught in ML 310 are currently doing the most visible work outside AI itself. Each is now its own standalone, phase-based curriculum — same catalog format as this document, with its own domain strand, DEG 300 substitution, and worked Phase II–III course sequence.
No end date here either, for a sharper reason than in the Verification Track: the field's own baseline moves roughly every conference cycle. This loop is the difference between a graduate who's current and one who's citing what they learned in school.