What you'll learn?
Eigen‑logic reveals the “Un‑Wiggle Line,” the axis where noise disappears and true patterns emerge. By following eigenvalues and eigenvectors, chaos becomes readable — risks in markets, signals in climate — turning algebra into a compass for structured insight.
Description
Course Outcomes:
- Construct Covariance Matrices from raw data sets to map the relationships between variables.
- Perform Principal Component Analysis (PCA) using Microsoft Excel/Google Sheets to extract the dominant trends from Stock Market and Climate data.
- Explain that "Risk" in finance and "Signal" in climate science are mathematically identical to "Variance" (Eigenvalues).
- Define the difference between Real Eigenvalues (Scaling) and Imaginary Eigenvalues (Rotation).
- Feel prepared and confident to tackle advanced linear algebra problems in university entrance exams (JEE/SAT) or engineering courses.
Course content
Total:
10 lectures
Total Duration:
2 hours, 39 minutes, and 47 seconds
the instructor
M SANJEEVA REDDY
Future-ready mathematics innovator, blending AI, machine learning, and timeless principles to make math accessible and empowering.