**Review by**

Ishan Phansalkar

**Course Offered In**

Autumn 2022

**Instructors**

Prof. Dwaipayan Mukherjee

**Prerequisites**

MA106: Linear Algebra (Not required but helpful)

**Difficulty**

Moderate

**Course Content**

Starts with the basics of linear algebra (vector spaces, invertibility), moves on to the Rank-Nullity theorem. Some time is spent on inner product spaces and the definition of norms. Then you’re introduced to the concept of linear maps, functionals, and Dual and Double Dual Vector Spaces. The second half focuses on A-invariant vector spaces and introduces Affine sets. Then the focus is shifted onto eigenvalues, eigenvectors and the Jordon form while introducing concepts like the similarity of matrices and diagonalisability. A few weeks are spent on minimal polynomials and the Cayley-Hamilton theorem, and finally, the course ends with a taste of Graph theory in the final lecture. Overall, involves a fair share of proofs and is more mathematical than MA106.

**Feedback on Lectures**

The course content is carefully planned and organized. The prof is receptive to questions and goes over the concepts again if asked. In all a very interactive teaching style focusing on understanding over problem-solving.

**Feedback on Evaluations**

The grading policy followed for the Autumn ‘22 offering of the course was:

- Assignments : 10% (5 x 2%)
- Quizzes : 15% (3 x 5%)
- Midsem : 25%
- Endsem : 50%

The assignments are lengthy and take a long time to solve but are also helpful from the exam pov. Contain about 20 fairlt involved questions each.

All the exams are slightly on the more challenging side but reasonably straightforward if you’re thorough with the basics and comfortable with proof writing.

**Study Material and References**

No slides; you’ll be required to take notes.

**Final Takeaways**

A really good course in linear algebra builds over stuff covered in MA106 and delves into some of the more abstract (read: interesting) parts of linear algebra.

**Grading Statistics:**