Tivadar Danka publishes a complete math roadmap for machine learning: linear algebra, calculus, probability
TivadarDanka · x · 2026-11-03
ML researcher Tivadar Danka shared a systematic math roadmap covering the three pillars behind machine learning.
- Linear algebra: describes the models themselves
- Calculus: fits models to data (understanding SGD requires multivariable calculus)
- Probability theory: provides the theoretical framework for prediction under uncertainty, tying it all together
He advises beginners to use the article as a reference map rather than reading it in one sitting: go deep on a concept, then return to the roadmap. The goal is taking readers from zero to understanding how neural networks work — with proper foundations, seemingly complex methods like SGD become quite natural.
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