Lecture Notes - Mathematical Methods in Data Science

• Syllabus
• Lecture 1 - Singular Value Decomposition
• Lecture 2 - Canonical Correlation Analysis
• Lecture 3 - Independent Component Analysis
• Lecture 4 - Mahalanobis Distance
• Lecture 5 - Laplacians and Diffusion
• Lecture 6 - Reproducing Kernel Hilbert Spaces
• Lecture 7 - Maximum Mean Discrepancy
• Lecture 8 - Random Projections
• Lecture 9 - Markov Chain Monte Carlo
• Lecture 10 - Permutation Tests
• Lecture 11 - EM and Variational Inference
• Lecture 12a - Dynamic Time Warping
• Lecture 12b - Dynamic Mode Decomposition
• Lecture 13 - Gaussian Process Regression
• Lecture 14 - Kalman Filters