Tutor for Math in Machine Learning (MML)
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
Project Title:
Expert Math Tutor Needed: Linear Algebra & Matrix Decompositions for Machine Learning (MML Book, Ch 2–4) the review sheets are here for chapter 2 to 4 https://drive.google.com/drive/folders/1Z_wQ6sliO1rtujjDm1Ep9ER5lApHw35E?usp=sharing
Project Description:
I am looking for an experienced mathematics or machine learning tutor to guide me through 1-on-1 online sessions covering Chapters 2, 3, and 4 of the textbook Mathematics for Machine Learning (by Deisenroth, Faisal, and Ong).
My goal is to gain deep conceptual intuition, master mathematical proofs/derivations, and build practical problem-solving skills for ML interviews and advanced study.
Topics to Cover:
Chapter 2: Linear Algebra — Systems of linear equations, Gaussian elimination, vector spaces & subspaces, linear independence, basis & rank, linear mappings, transformation matrices, basis change, kernel/null space, and affine spaces.
Chapter 3: Analytic Geometry — Vector norms, inner products, lengths & distances, angles, orthogonality, orthonormal bases (Gram-Schmidt), orthogonal projections, normal equations, and rotations.
Chapter 4: Matrix Decompositions — Determinants & trace, eigenvalues & eigenvectors, Cholesky decomposition, spectral theorem, Singular Value Decomposition (SVD), and low-rank matrix approximations.
Responsibilities & Session Format:
Live 1-on-1 screen sharing(via freelancer)
Walk through complex theoretical derivations, step-by-step matrix computations, and textbook practice problems.
Clarify core intuitions (e.g., geometric interpretations of SVD, orthogonal projections, and basis transformations).
Answer specific Q&A on practice exercises and interview-style questions.
Ideal Freelancer Qualifications:
Degree in Mathematics, Applied Mathematics, Computer Science, Data Science, or related quantitative field.
Deep mastery of university-level Linear Algebra and mathematical foundations of Machine Learning.
Familiarity with the Mathematics for Machine Learning (MML) textbook is a huge plus.
Fluent English with clear communication skills and patience for step-by-step explanations.
Expert Math Tutor Needed: Linear Algebra & Matrix Decompositions for Machine Learning (MML Book, Ch 2–4) the review sheets are here for chapter 2 to 4 https://drive.google.com/drive/folders/1Z_wQ6sliO1rtujjDm1Ep9ER5lApHw35E?usp=sharing
Project Description:
I am looking for an experienced mathematics or machine learning tutor to guide me through 1-on-1 online sessions covering Chapters 2, 3, and 4 of the textbook Mathematics for Machine Learning (by Deisenroth, Faisal, and Ong).
My goal is to gain deep conceptual intuition, master mathematical proofs/derivations, and build practical problem-solving skills for ML interviews and advanced study.
Topics to Cover:
Chapter 2: Linear Algebra — Systems of linear equations, Gaussian elimination, vector spaces & subspaces, linear independence, basis & rank, linear mappings, transformation matrices, basis change, kernel/null space, and affine spaces.
Chapter 3: Analytic Geometry — Vector norms, inner products, lengths & distances, angles, orthogonality, orthonormal bases (Gram-Schmidt), orthogonal projections, normal equations, and rotations.
Chapter 4: Matrix Decompositions — Determinants & trace, eigenvalues & eigenvectors, Cholesky decomposition, spectral theorem, Singular Value Decomposition (SVD), and low-rank matrix approximations.
Responsibilities & Session Format:
Live 1-on-1 screen sharing(via freelancer)
Walk through complex theoretical derivations, step-by-step matrix computations, and textbook practice problems.
Clarify core intuitions (e.g., geometric interpretations of SVD, orthogonal projections, and basis transformations).
Answer specific Q&A on practice exercises and interview-style questions.
Ideal Freelancer Qualifications:
Degree in Mathematics, Applied Mathematics, Computer Science, Data Science, or related quantitative field.
Deep mastery of university-level Linear Algebra and mathematical foundations of Machine Learning.
Familiarity with the Mathematics for Machine Learning (MML) textbook is a huge plus.
Fluent English with clear communication skills and patience for step-by-step explanations.
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