Computational Methods for Data Science, Jan-May 2025
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 "2024_cmds" or "2024_cmds-group@smail.iitm.ac.in". Click on it and request to be added to the group.
Exams and Grading
| Evaluation |
Points |
Date |
| Assignments |
20 |
Fortnightly |
| Quiz-I |
20 |
Feb 20th, Thursday |
| Quiz-II |
20 |
Mar 27th, Thursday |
| EndSem |
40 |
May 13th, Tuesday |
Assignments and Reading materials
Click here
- All assignments need to be submitted before Sunday midnight.
- Students are strongly encouraged to typeset their solution via LaTeX/TeX. Typesetting using LaTeX/TeX will obtain 25% bonus on the score they get for the assignment.
- Students need to submit their assignments through the dropbox link provided.
- The name of the zipped file (this should contain the LaTeX/TeX source file, pdf, and code) for submitting your assignment should be as follows: ma14c093_5.zip (or .rar or .tar), where ma14c093 is your roll number and 5 implies that you are submitting your fifth assignment.
- Any copying on assignments will result in a zero on the assignment.
- We will be using JPlag to detect similarities among multiple sets of source code files.
- The grader will expect you to express your ideas clearly, legibly, and completely, often requiring complete English sentences rather than merely just a long string of equations or unconnected mathematical expressions. This means you could lose points for poorly written proofs or answers. Clear exposition is a crucial ingredient of technical communication. Clarity of thought and presentation is more important in mathematics & sciences than any other field. The only way to master exposition is by repeated practicing.
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