An introduction to intermediate level topics in Linear Algebra by a series of probing questions and answers. Specifically the following topics are covered:
All materials including videos, notes, assignments are provided here. The textbooks used are also openly available. The course was designed for computer science and electronics undergraduate engineering students at IIIT Hyderabad.
reviews! not so bad i guess.
— Girish Varma (@girishvarma) May 3, 2021
amazed by students who took time to solve hard problems even during these times. pic.twitter.com/5sgsiRY7oZ
Assumes that Linear Algebra I is covered. Specifically assumes knowledge of the following topics:
From 30th March to 4th May, 2021, Tuesdays, Thursdays and Saturdays
Weekly tutorials conducted by TAs in smaller groups as per times fixed.
I will be available on Teams during the timings given bellow, for clearing any doubts. You can sent a direct message to me for joining.
Students are expected to spent atleast 12 hrs per week. Roughly
Your intelligence cannot be measured by just a number. It is defined by your willingness to learn, solve problems and try new things.
— Prof. Feynman (@ProfFeynman) April 14, 2021
You are more than just a number. Develop your skills wherever they may lead. Share your ideas. Your skills are more valuable than your grades. ðŸ§
The resources provided are licenced under Creative Commons/Open Licences and hence downloadable.
[CDTW] Linear Algebra
David Cherney, Tom Denton, Rohit Thomas and Andrew Waldron
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W. Keith Nicholson
[MR] Interative Linear Algebra
Dan Margalit and Joseph Rabinoff
[DA] Understanding Linear Algebra
David Austin
Down with Determinants
Sheldon Axler
Linear Algebra Done Right Videos
Sheldon Axler
Markov Chains and Google’s PageRank Algorithm
Understanding Linear Algebra, David Austin.
The Fast Fourier Transform and Polynomial Multiplication
Mordecai GOLIN
[GS] Linear Algebra MIT OCW Course Materials
Gilbert Strang
Richard Feynman, His Life, and the Art of Solving Important Problems
Blog Post
How to Solve it
G. Polya
Shorter Version
Recalling Basics | Function Spaces | Random Walk on Graphs
Definitions | Characteristic Polynomial | Examples
Eigenvector Basis and Powering | Multiplicities
Submit by 13th April
Jordan Form | Norms | Distance | Inner Product | Complex Case
Orthogonal & Orthonormal Vectors | Gram-Schmidt Orthogonalization
Subspace Projections | Orthogonal Complements | Fitting with Errors
Best fit vector on a subspace | Least Squares Fitting Equation
Submit by 26th April
Eigenvalues and eigenvectors of Symmetric Matices | Spectral Theorem
Spectral Theorem | Spectral Decomposition
Spectral Theorem for Complex Spaces | Singular Value Decomposition
Principal Component Analysis | Applications in Data Science
See Section 6 in Problems. Submit by 7th May.
On 4th May