PURPOSE

We’re not here to “learn math.”
We’re here to build a mental architecture for understanding compression, transformation, and meaning — the invisible scaffolding behind Machine Learning, physics, economics, and reality itself.

This is not “school algebra.”
This is symbolic survival.

THE JOURNEY

This series is split into four ascending phases:

Phase 1 — Linear Algebra as Symbolic Grammar

We start from the core. What is a vector? What does it mean to be transformed by a matrix? What survives transformation?

Here, algebra becomes geometry. Shapes. Forces.
Your intuitions will shift.

Phase 2 — Probability as Uncertainty Modeling

From shapes to shadows:
What is randomness, really? How do we model ignorance, and how does that shape belief?

Phase 3 — Optimization as a Search for Meaning

Math gets hungry. Systems want something.
We begin to talk about loss, desire, and learning.

Phase 4 — Machine Learning Core Ideas

We close with the systems everyone talks about — but now with depth.

WHO IS THIS FOR?

You, if:

• You want to understand ML without drowning in hype or code.
• You think in symbols, shapes, feelings, and relations — not just formulas.
• You want a long-lasting foundation, not copy-paste tricks.
• You believe abstraction is power.