📅 2024-10-31 — Session: Explored SVD for Image Compression and Teaching
🕒 17:35–19:10
🏷️ Labels: SVD, Image Compression, Teaching, Numerical Methods, Data Analysis
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal:
The session aimed to explore various numerical methods and their applications, focusing on Singular Value Decomposition (SVD) for image compression and teaching strategies.
Key Activities:
- Cooking Recipes: Briefly reviewed recipes using harina de mandioca.
- Polynomial Approximation: Reflected on using normal equations for approximating the sine function.
- Least-Squares Problem: Planned the use of orthogonal factorizations for solving least-squares problems, emphasizing numerical stability.
- SVD Image Compression: Developed a structured outline for a notebook on SVD-based image compression.
- Teaching SVD: Planned an educational approach to teaching SVD and image compression, focusing on theory and practical exercises.
- SVD and PCA Equivalence: Reflected on the equivalence between SVD and PCA, highlighting efficient data analysis techniques.
Achievements:
- Created a comprehensive outline for an SVD image compression notebook.
- Developed a teaching strategy for SVD in image compression.
- Clarified the equivalence of SVD and PCA for dimensionality reduction.
Pending Tasks:
- Implement the SVD image compression notebook.
- Develop teaching materials based on the planned strategy for SVD.
- Further explore numerical stability in polynomial approximations.