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Tutor for Math in Machine Learning (MML)

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placeUS home_workRemote assignmentBefristet publicAggregierter Job · US

eventVeröffentlicht am 17. Okt. 2026 · verifiedWir haben am 18. Okt. 2026 bestätigt, dass er noch aktiv ist

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US$ 2 – US$ 8 pro Projekt

Über den Job

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.

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