Course Catalog & Credential Pathway — Dwg. No. PF-002G
A field branch of the Construction Track, for students aimed at the physical world — a policy that has to survive contact with an actual body, not just a benchmark.
Field branch of Dwg. No. PF-002, The Construction Track — same core sequence through Phase II, concentrated here into control systems and embodied learning from Phase II onward. See also Dwg. No. PF-001, The Verification Track.
Five core strands, plus the domain strand this branch concentrates in
No hardware yet, and no simulators yet either. Two years of straight math and programming fundamentals, identical to the core Construction Track.
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, same as the core track. The hardware layer begins concurrently — by the end of Phase II a student can both train a small model and derive the inverse kinematics for a robot arm by hand.
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.
Control Systems & Kinematics
Forward and inverse kinematics, feedback control, and the classical robotics stack that a learned policy still has to hand off to at the actuator.
MATH 210 (concurrent)
Sensorimotor Learning & Simulation
Simulation-to-real transfer, domain randomization, and the imitation- and reinforcement-learning methods used to train a policy before it ever touches real hardware.
ENG 220, ML 230
Here the domain degree takes over from the general CS/applied-math track. A robotics or mechanical engineering-plus-CS program replaces DEG 300 — hardware fluency is what separates a robotics hire from an ML generalist.
Robotics / Mechanical Engineering Degree
Formal core in a robotics or mechanical engineering-plus-CS program — hardware fluency is what separates a robotics hire from an ML generalist.
Vision-Language-Action Models
GR00T-style foundation models for embodied agents — instruction following, multimodal perception, and the safety envelope a physical robot needs that a chatbot never did.
ROB 230
Deep Learning for Perception & Control
Vision backbones, policy architectures, and the imitation- and reinforcement-learning methods behind embodied agents, taught directly against a manipulation or locomotion benchmark rather than as a generic survey.
ML 230
Systems for ML
Distributed training, GPU/accelerator programming, and the infrastructure a large-scale simulation fleet needs to generate enough sim-to-real training data at any real breadth.
CS 210
Supervised Research Practicum
Placement inside a robotics lab with real hardware, 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 policy or system — a manipulation skill, a navigation stack — taken from idea to a deployed, load-bearing artifact tested against real hardware.
ENG 320, RES 350
No end date: the ML baseline moves every conference cycle, and the hardware baseline — new actuators, new sensors, new simulation platforms — moves on its own industry schedule that a purely computational graduate can lose track of.