Is this the right course for ML?

Yes — matrix calculus, SVD, PCA, and least squares are core. We tailor to your ML syllabus.

Linear Algebra tutoring

Matrices are transformations. Once you see that, everything opens up.

Taught the Strang way — geometric intuition first, then the algebra. From row reduction through the singular value decomposition and applications in ML and physics.

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Levels covered

Introductory (Math 54 / MATH 240)
Second-course (proof-based)
Applied for ML/data science
Grad-school prep

Topics we cover

The full linear algebra syllabus.

Vectors & vector spaces

V = \text{span}(v_1, \ldots, v_n)

Matrix multiplication

(AB)_{ij} = \sum_{k} A_{ik} B_{kj}

Determinants

\det(AB) = \det(A)\det(B)

Eigenvalues

Av = \lambda v

Rank-nullity

\dim(\ker T) + \dim(\text{im } T) = \dim V

SVD

A = U \Sigma V^{\top}

Sample problem

Diagonalise

A = \begin{pmatrix} 2 & 1 \\ 1 & 2 \end{pmatrix}

Find the eigenvalues and eigenvectors of A, and write A = PDP⁻¹.

Exam preparation

We prep you for:

MIT 18.06Berkeley Math 54IB HL AA optionGRE Math SubjectUniversity finals

FAQ

Linear Algebra tutoring — questions

Very common. We bridge from computation to proof by revisiting familiar theorems (rank-nullity, spectral) with rigor.

Linear Algebra

Ready to make linear algebra click?

Tell us your course and goals. We'll match you with the right tutor and put a first session on the calendar.

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