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Mathematical Foundations of Machine Learning →0/7
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The Geometry of Linear Equations

Gilbert Strang · 39:49

2

Transposes, Permutations & Vector Spaces R^n

Gilbert Strang · 47:41

3

Convex Functions — First/Second-Order Conditions & Epigraphs

Stephen Boyd · 1:17:14

4

Saddle-Point Interpretation, KKT Conditions & Perturbation

Stephen Boyd · 1:09:19

5

The Learning Problem

Yaser Abu-Mostafa · 1:21:27

6

Is Learning Feasible?

Yaser Abu-Mostafa · 1:16:48

7

Building makemore Part 3: Activations, Gradients, Kaiming Init & BatchNorm

Andrej Karpathy · 1:55:57

Transposes, Permutations & Vector Spaces R^n

Gilbert StrangDistinguishedMIT

Professor of Mathematics

47:41·2009·undergraduate level
Path step 2: Develops vector spaces and subspaces needed for orthogonal projections.

Marks the transition from row operations to genuine vector-space geometry: symmetric matrices RᵀR, P⁻¹ = Pᵀ for permutation matrices, subspaces of ℝⁿ, and closure under linear combinations.

MathematicsLinear AlgebraVector SpacesSubspacesSymmetric Matrices

MIT 18.06 Linear Algebra, Spring 2005

5. Transposes, Permutations, Spaces R^n

Source: MIT OpenCourseWare via YouTubeWatch on YouTube
LecturerAll lecturers →
Gilbert StrangDistinguished

MIT Professor Emeritus & Author of Introduction to Linear Algebra

MIT Professor of Mathematics Emeritus whose legendary 18.06 Linear Algebra and 18.085 Computational Science courses educated millions of engineers and mathematicians.

3 courses · 56 lecturesCanon
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The Geometry of Linear Equations

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Convex Functions — First/Second-Order Conditions & Epigraphs

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