Basic Information
- Course Code: EE 635
- Course Name: Applied Linear Algebra
- Course Offered In: 2023-‘24
- Semester Season: Autumn
- Instructors: Dwaipayan Mukherjee
- Prerequisites: None. Although it builds up on the concepts of MA106. So having strong grasp on MA106 concepts, will smoothen the course a lot.
- Difficulty (1 being easy and 5 being tough): 3
Course Content
Vector spaces, Linear dependence, Basis and it’s Transformation, Linear Functionals, Isomorphism, Orthogonal Projections, Polynomials, Quotient Space, Invariant subspaces, Matrix polynomial, Jordan form and its applications.
Feedback on Lectures
The Prof was very enthusiastic about the lectures, although he would go at a slightly quicker pace. So it was important to maintain focus and make notes for the whole lecture, because loosing concentration even for 5-10 mins, will result in you not understanding rest of the whole lecture. Prof would give detailed proof of every theorem which was really helpful in understanding the related concepts.
Feedback on Evaluations
3 quizzes( best 2 were considered)- 15%. 5 Assignments - 15% Midsem - 25% Endsem - 45%.
Each Assignment used to contain 30-40 questions, out of which we had to 10-15 questions, selected by prof. For evaluation. But solving all the questions of assignment was really helpful as almost 80-90% of midsem and endsem consisted of questions of assignments, which weren’t part of question set selected by prof. So, exams were on easy to moderate in difficulty, provided one is clear with concepts used in assignment questions
Study Material and Resources
No books were followed as such.
Follow-up Courses
There is not any particular follow up course for this. But after this, you can take up math heavy control courses like EE640, EE636 as linear algebra is significantly used in these courses.
Final Takeaway
The course is full of abstract proofs and theorem. Although name contains “applied”, but don’t expect it to be on application side. One should take this course if he/she wants to pursue controls domain or ML, as concepts of this course is used in these domains extensively.