What you'll learn?
Learners will build covariance matrices, compute eigenvalues, and project data onto principal components. They will apply power iteration for web ranking, study quantum Hamiltonians for stability, and validate vibration modes in engineering. Eigen-analysis will help them extract biological markers, uncover hidden topics in text, detect cyber-attacks, and compress 3D game assets.
Description
Course Outcomes:
- Implement PCA and SVD from scratch using Python/NumPy.
- Compress images and reduce dataset dimensions efficiently.
- Interpret Principal Components to extract actionable insights.
- Apply matrix decomposition for noise reduction and recommendations.
- The math behind Covariance Matrices and Eigen decomposition.
- The difference between Feature Selection and Feature Extraction.
- The geometry of Orthogonal Projection and Basis Changes.
- The connection between Eigenvalues, Singular Values, and Variance.
- Confident navigating high-dimensional data spaces.
- Empowered to "open the black box" of AI algorithms.
- Intimidated by complex datasets or matrix notation.
Course content
Total:
11 lectures
Total Duration:
3 hours, 6 minutes, and 25 seconds
the instructor
M SANJEEVA REDDY
Sanjeeva Reddy leads mathematics curriculum innovation, blending timeless principles with modern technologies like AI, machine learning, and data-driven strategies. His programs prepare learners to tackle global challenges with confidence. With expertise in stakeholder collaboration and project execution, he makes mathematics accessible, empowering, and deeply relevant for lifelong learning