E0 299: Computational Linear Algebra


  • Announcements:

  1. Registration is strictly restricted to MTech (AI) 2020-2022. (others are welcome to audit).

  2. The first online meeting is on October 1 from 3:30 - 4:30 pm.

  3. The final exam is on January 30, 10:00-13:00 hrs.

Course Details

  • Term: October 2020 - January 2021.

  • Credits: 3:1.

  • Webpage: https://tinyurl.com/CompLinAlg

  • Hours: Wednesday, Friday: 5:00 - 6:30 pm.

  • Instructor: Kunal N. Chaudhury (kunal@iisc.ac.in).

  • Teaching Assistants: Ruturaj Gavaskar, Pravin Nair, Unni VS, Tripti Jain.

  • Grading: math problems (15%), coding assignments (15%), continuous assessment/tests (40%), final exam (30%).

  • Objective: To provide a good mix of geometric intuition, theorem proving, and how things can be computed efficiently. This is really an introductory course on linear algebra with a focus on theory, but we will also touch upon some algorithmic aspects without getting into details (coding assignments will be given in this regard).

  • Syllabus:

Theory: Solution of linear equations, vector space, linear transformations, matrix representation, inner-products and norms, orthogonality and least-squares, trace and determinant, eigendecomposition, symmetric (Hermitian) matrices and quadratic forms, singular value decomposition.

Computations: linear solvers, least squares, QR (Gram-Schmidt), SVD.

  • Textbooks:

  1. S. Axler, Linear Algebra Done Right, Springer, 2015.

  2. G. Strang, Linear Algebra and its Applications, Brooks/Cole, 2006.

  3. L. Trefethen and D. Bau, Numerical Linear Algebra, SIAM, 1997.

  • Web resources:

  1. http://ocw.mit.edu/18-06S05 (video lectures by Strang)

  2. http://vmls-book.stanford.edu/ (linear algebra applications)

  3. http://immersivemath.com/ila/index.html (nice visualizations)

  4. https://math.stackexchange.com/?tags=linear-algebra (lots of Q/A)