Machine Learning Prerequisites Map #
201 concepts organized by true dependency order across CS, mathematics, and machine learning - with learning cost metrics and interactive visualizations.
201
Concepts
14
Categories
34
Interactive Lessons
21
Entry Points
[## Browse All Concepts →
Sortable table with learning cost metrics, prerequisites, and ROI rankings.](/tech-tree/inventory/)[## View Dependency Graph →
Interactive visualization showing how all 201 concepts connect.](/tech-tree/graph/)
Why This Exists #
Most ML learning paths follow academic tradition - Calculus I, then Calculus II, then Linear Algebra. But gradient descent does not care about course catalogs. It needs partial derivatives, matrix multiplication, and the chain rule simultaneously. This map shows what actually depends on what.
201 concepts organized by true dependency order. Every concept connects to prerequisites below it and unlocks capabilities above it. Find where you are, see what you need next, skip what you do not.
What Makes This Different #
Learning Cost Metrics #
Each concept includes quantified metrics: how many atomic elements you need to learn, the total prerequisite depth, and how many downstream concepts it unlocks. High fan-out concepts give better ROI than dead ends.
Interactive Visualizations #
34 concepts include animated canvas visualizations. These are a work in progress. If something looks rough or you have feedback - let me know.
Categories #
Probability & Statistics32 conceptsLinear Algebra19 conceptsCalculus15 conceptsDiscrete Math14 conceptsGraph Theory12 conceptsOptimization12 conceptsMachine Learning37 conceptsInformation Theory7 conceptsGame Theory12 conceptsAlgorithms18 conceptsData Structures10 conceptsFormal Methods2 conceptsSoftware Engineering1 conceptsApplied Economics10 concepts
Where to Start #
If you're new to the math behind ML, start with these high-ROI entry points - concepts with low prerequisite cost but high downstream unlock count:
Feedback Welcome #
This is an active project - the dependency graph, visualizations, and lessons are all evolving. If you find something wrong, have a suggestion, or want to say what was useful - xavi@hostari.com or LinkedIn.