📅 2024-09-19 — Session: Developed Course Structure for Computational Linear Algebra
🕒 15:30–16:20
🏷️ Labels: Course Design, Linear Algebra, Education, Python, Eigenvalues
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to develop a comprehensive course structure for a Computational Linear Algebra class, integrating both theoretical concepts and practical applications.
Key Activities
- Developed an initial framework for the course, emphasizing the introduction of theoretical concepts alongside practical applications using Python libraries such as NumPy and SciPy.
- Proposed a detailed class structure covering basic to advanced topics, including LU decomposition, QR decomposition, eigenvalues, eigenvectors, and iterative methods.
- Outlined a structured class plan for teaching eigenvalues and eigenvectors, incorporating Python implementations and interactive exercises.
- Planned a class on PA=LU decomposition and Leontief models, concluding with eigenvalues and eigenvectors.
Achievements
- Established a foundational curriculum framework for the Computational Linear Algebra course.
- Created detailed lesson plans for key topics, ensuring a balance between theory and practical exercises.
Pending Tasks
- Further refinement of the curriculum to include more interactive components and assessments.
- Development of additional Python exercises and visualizations to enhance student engagement.