Developed Course Structure for Computational Linear Algebra

• Day: 2024-09-19
• Time: 15:30 to 16:20
• Project: Teaching
• Workspace: WP 1: Strategic / Growth & Development
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Course Design, Linear Algebra, Education, Python, Eigenvalues

Description

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.

Evidence

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• event_ids: []