STE001: How to Read Math Notation

Build fluency in symbols and notation used in machine learning and research: Greek letters, indexing, sums and sets, norms, calculus, probability, and full equation decoding.

01

Greek Letters & Common Symbols

Week 1 · Module 1 · Lesson 1

Recognize frequently used Greek letters and symbols and what they often mean in papers.

Lesson slides

M1 · L1 — Greek Letters & Common Symbols

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02

Subscripts, Superscripts & Indexing

Week 2 · Module 1 · Lesson 2

Read stacked indices clearly: which dimension or object each subscript or superscript refers to.

Lesson slides

M1 · L2 — Subscripts, Superscripts & Indexing

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03

Summation, Product & Set Notation

Week 3 · Module 1 · Lesson 3

Unpack sums, products, and set-builder notation the way authors use them in derivations.

Lesson slides

M1 · L3 — Summation, Product & Set Notation

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04

Norms, Distances & Regularisation

Week 4 · Module 1 · Lesson 4

Connect norm symbols to geometry in parameter space and common regularisers.

Lesson slides

M1 · L4 — Norms, Distances & Regularisation

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05

Calculus Notation in ML/RS

Week 5 · Module 1 · Lesson 5

Follow gradients, partial derivatives, and chain-rule style notation in optimization sections.

Lesson slides

M1 · L5 — Calculus Notation in ML/RS

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06

Probability & Statistics Notation

Week 6 · Module 1 · Lesson 6

Decode expectations, distributions, and common statistical shorthand in papers.

Lesson slides

M1 · L6 — Probability & Statistics Notation

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07

Full Equation Decoding

Week 7 · Module 1 · Lesson 7

Put it together: read dense equations line by line and restate them in plain language.

Lesson slides

M1 · L7 — Full Equation Decoding

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