Mathematics of Bigdata Analytics
A comprehensive short course series on the mathematical foundations of big data analytics
| Date | Title | Description |
|---|---|---|
| January 13, 2026 | Module II Ends |
Module II: Concentration of Probability Measures has been completed |
| January 6, 2026 | Module II Begins |
Module II: Concentration of Probability Measures begins on January 6, 2026. |
| January 1, 2026 | Module I Completed |
Module I: Dimension Reduction has been completed. |
Module I: Dimension Reduction
Theory and Applications to Image Processing. Covers Spectral Decomposition, SVD, covariance, geometric projections, and Principal Component Analysis (PCA) with applications in image processing.
CompletedModule II: Concentration of Probability Measures
Derivation of concentration inequalities (Markov, Chebyshev, Chernoff), sub-Gaussian and sub-exponential variables, Johnson-Lindenstrauss lemma, and Berry-Esseen theorem.
CompletedModule III: Randomized Algorithms for Linear Algebra (RALA)
Sampling-based approaches for matrix operations, matrix sketching, SVD, CUR decomposition, and fast randomized methods for large-scale least squares problems.
UpcomingModule IV: Inverse Problems in Empirical Modeling
Solving linear/nonlinear, over/under determined inverse problems using matrix decomposition (LU, Cholesky, QR, SVD) and optimization methods (Gradient, Conjugate gradient, Quasi-Newton).
In Progress