Scientific Computing & Advanced Programming Lab

Lecture and Lab schedule - Mondays and Wednesdays (2:00 - 4:45 pm)

TA: Rohit Kumar Mani, contact: ma24d015@smail.iitm.ac.in

Venue - KCB-520

Announcements:.

Project submission deadline: 5th November, 2026 (Thursday)

Viva exams: 5th, 6th and 7th November, 2026 Schedule

Codes

1. 2 by 2 Linear solver code

Lecture Slides / Notebooks

1. Topics: Introduction to Python programming Slides-1
2. Topics: List and Tuples Slides-2
3. Topics: Object oriented programming Slides-3
4. Topics: Inheritance Slides-4
5. Topics: Operator overloading, NumPy Notebook-5
6. Topics: Matrix inversion Slides-6
7. Topics: Eigenvalues, Eigenvectors and SVD Slides-7
8. Topics: SVD via NumPy Notebook-8
9. Topics: Unconstrained Optimization Slides-9 Notebook-9
10. Topics: EM Algorithm Slides-10
11. Topics: Automatic Differentiation Notebook-11
12. Topics: Constrained Optimization Slides-12
13. Topics: Central Limit Theorem Slides-13, Notebook-13
14. Topics: Version Control Slides-14
15. Topics: Monte Carlo experiments Notebook-15a, Notebook-15b
16. Topics: Benchmarking, Profiling, and Performance-Optimization Notebook-16

Practice Problems

1. Topics: Strings, loops, Practice Problems-1
2. Topics: control flows Practice Problems-2
3. Topics: Classes Practice Problems-3
4. Topics: Nested loops Practice Problems-4
5. Topics: Debbug the codes (put appropraite test statements wherever necessary and check the maths as well) Gaussian-elimination, Newton-Raphson, Newton-Bisection
6. Topics:EigenValues and Vectors Practice Problems-5 data.txt
7. Topics: Unconstrained and constrained optimization Practice Problems-6, curve_fitting_data.txt
8. Topics: Central Limit Theorem Practice Problems-7

Books / Resources / References

1. MIT OpenCourseWare Introduction to Computer Science and Programming in Python by Ana Bell
2. Python for Scientific Computing