II Semester 2025-26
Content: Number representation, fundamentals of error analysis, conditioning, stability, singular value decomposition and its applications, QR factorization, condition number, least squares and regression, Gaussian elimination, eigenvalue computations and applications, iterative methods, root finding, elements of convex optimization including steepest descent and Newton’s method.
Textbooks:
Primary references:
Other suggested textbooks:
Prerequisites: COL106 or equivalent. Familiarity with linear algebra and calculus is assumed. You will be expected to write programs in Python or MATLAB.
Overlaps with: MTL704.
Announcements: All announcements will be made on Piazza. It is your responsibility to check it regularly.
Lecture topics and relevant textbook chapters will be listed here as the semester proceeds.
Evaluation:
Students should use either Python 3 (with Numpy/Scipy) or MATLAB for the programming component of the homework. Some online tutorials and references for Numpy:
Late policy: Homework assignments are due at midnight on the due date. You are allowed a total of 4 late days across all the assignments, counted with a granularity of 0.5 days (i.e. submission times are rounded up to the next noon or midnight). Any submissions exceeding these limits will not be graded.
Audit pass requirements: At least 75% attendance, 50% marks in the course total, and 20% marks in each homework, quiz, and exam.
Attendance policy: Attendance lower than 75% will result in a one-grade penalty (e.g. A to A–, or A– to B).
Academic dishonesty: Any cases of plagiarism or other unfair means will result in strict disciplinary action and referral to the CSE department’s disciplinary committee.