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MH2802 - Linear Algebra for Scientists

Lecture Clarity:
(4/5)
Content Relevance:
(4/5)
Content Difficulty:
(4/5)
Overall Workload:
(3/5)
Team Dependency:
(1/5)

Course Summary​

MH2802 is a mathematically rigorous Linear Algebra module designed primarily for students in Physical and Computing Sciences. The course covers fundamental and advanced topics including:

  1. Vector Algebra & Analytical Geometry
  2. Linear Spaces and Subspaces
  3. Inner Products and Orthogonality
  4. Linear Transformations and Matrix Representations
  5. Matrix Algebra and Systems of Equations
  6. Eigenvalues and Eigenvectors
  7. Special Matrices (e.g., Hermitian, Unitary)
  8. Applications to Physics and Computing Science, such as coupled oscillators and Markov chains While the material is quite abstract and math-heavy, the course coordinator, Prof. Mile Gu, explains concepts clearly and derives them from first principles. Once the core ideas click, many find the content engaging and relevant, especially due to its real-world applications.

Workload​

The workload is moderate and reasonable for a 3AU module. Weekly commitments include:

  • One 2-hour lecture focusing on derivations and problem-solving
  • One 1-hour tutorial that works through examples, often more challenging than pre-lesson assessments
  • Weekly quizzes or pre-lesson assessments (auto-graded MCQs and short problems) that reinforce lecture content The tutorials and quizzes are essential for grasping the material, though some students note that tutorial difficulty may not fully match the final exam’s challenge. The content accumulates quickly, so consistent weekly study is advised to avoid last-minute cramming.

Projects​

There are no group projects in this module. Assessment components include:

  • Pre-lesson assessments (12%) consisting of auto-graded quizzes to strengthen foundational concepts
  • Midterm test (28%), which is generally straightforward and covers vector algebra and linear spaces; many students score highly here, especially if they attempt bonus questions
  • Final exam (60%), which is known to be challenging, theory-heavy, and time-pressured, combining conceptual and computational questions Most learning is derived from lectures, tutorials, and practice rather than project work.

Tips to Do Well​

  • Take weekly quizzes seriously rather than rushing through them, as they solidify your understanding before tutorials. Avoid relying on shortcuts like ChatGPT for these.
  • Complete all tutorial questions, even if tutorials themselves are optional; these questions closely resemble exam problems.
  • Practice extensively with past year papers, especially for the finals, since exam questions tend to be similar or slightly harder.
  • Attempt bonus questions in both midterms and finals to gain extra marks.
  • Don’t cram—spread your revision throughout the semester to keep up with the fast-paced content.
  • If possible, seek additional practice questions from TAs or seniors, as official past year papers are limited.
  • Prof. Mile Gu is approachable and supportive, so don’t hesitate to reach out for help.

Based on reviews by GIS, CK