Mathematical Foundation for Data Science
Course, DSAI, IITM, 2025
Description
The course was intended to introduce students to the fundamental mathematical concepts required for a program in data science. As a TA, I had to conduct exam and evaluate them.
Course Content
The following topics was covered, but not necessarily in the order listed below:
- Vector spaces
- Linear equations
- Orthogonality
- Eigen values and vectors
- Basics of Probability
- Discrete and continuous random variables
- Joint, marginals and conditionals
- Multivariate Normal
- Laws of large numbers and central limit theorem
- Unconstrained and constrained optimisation
- Gradient methods for unconstrained optimisation
- Theory of Lagrangians for constrained optimisation.
- Algorithms for constrained optimisation.
- Applications of Linear algebra, Probability and Optimisation in data science: Random projections, Principal component analysis.
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