Computational Methods for Data Science, Jan-May 2026
Credits 10 credits
Instructor Sivaram Ambikasaran, 
Classroom NNAC \(632\)
Timings As discussed in class
Mailing list
You can find on google groups when you login via smail. Search for "cmds" or "cmds-group@smail.iitm.ac.in". Click on it and request to be added to the group.
Exams and Grading
| Evaluation |
Points |
Date |
| Midsem |
40 |
March 13th, Friday |
| EndSem |
60 |
May 12th, Tuesday |
Assignments and Reading materials
Click here
Short Syllabus
Dominant part of the course will focus on matrix computations; roughly 35-40 lectures; Remaining 10-15 lectures will focus on function approximations;
- Matrix Computations: Fundamentals of Matrix Algebra; Floating point arithmetic; Conditioning of a problem; Forward and backward stability of algorithms; Algorithmic complexity; Matrix decompositions: LU, Cholesky, QR, Eigen decomposition, SVD; direct and iterative techniques.
- Function Approximation: Interpolation and Error, Hermite interpolation, Piecewise polynomial (Spline) interpolation, Other function approximations.
Textbooks
- Applied Numerical Linear Algebra, by James W. Demmel
- Numerical Linear Algebra, by Nick Trefethen
- Accuracy and Stability of Numerical Algorithms by Nicholas J. Higham
- Approximation theory and approximation practice by Nick Trefethen
- Fundamentals of Engineering Numerical Analysis by Parviz Moin