Course Catalog & Credential Pathway — Dwg. No. PF-002H
A field branch of the Construction Track, for students aimed at a model's output landing on a living system — soil, weather, a crop — not a screen.
Field branch of Dwg. No. PF-002, The Construction Track — same core sequence through Phase II, concentrated here into remote sensing and precision-agriculture systems 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 field data yet, and no imagery 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 agronomy layer begins concurrently — by the end of Phase II a student can both train a small model and explain what a nutrient deficiency looks like in a leaf.
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.
Plant & Soil Science Fundamentals
Crop physiology, soil chemistry, and the agronomic signals — nutrient stress, disease, water deficit — a vision model is actually being asked to detect.
MATH 201 (concurrent)
Remote Sensing & Yield Modeling
Multispectral and hyperspectral imagery, vegetation indices, and the statistical yield models these feed — the layer between a raw satellite pass and a field-level recommendation.
BIO 220, STAT 220
Here the domain degree takes over from the general CS/applied-math track. An agricultural science or plant biology-plus-CS program replaces DEG 300 — agronomic judgment is what makes a precision-ag hire trustworthy to an actual grower.
Agricultural Science / Plant Biology Degree
Formal core in an agricultural science or plant biology-plus-CS program — agronomic judgment is what makes a precision-ag hire trustworthy to an actual grower.
Autonomous Field Robotics & Precision Intervention
Computer-vision-guided variable-rate application and autonomous weeding and harvesting equipment, plus the field-trial methodology needed to validate a model's recommendation before it's applied at scale.
AGR 230, ML 230
Deep Learning for Remote Sensing
Vision architectures for multispectral and hyperspectral imagery, taught directly against a real yield-prediction or crop-stress benchmark rather than as a generic survey.
ML 230
Systems for ML
Distributed training and data pipelines at the scale a season's worth of satellite and drone imagery across thousands of fields actually demands.
CS 210
Supervised Research Practicum
Placement inside a precision-agriculture company or ag-extension research program, 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 system — a yield model, a variable-rate application pipeline — taken from idea to a deployed, load-bearing artifact validated against a real field trial.
ENG 320, RES 350
No end date: the ML baseline moves every conference cycle, and the agronomic baseline — new cultivars, new sensor platforms, a season's weather — moves on its own field calendar that a purely computational graduate can lose track of.