| Sl.No | Chapter Name | MP4 Download |
|---|---|---|
| 1 | Lecture 01 : Vector Spaces | Download |
| 2 | Lecture 02 : Vector Subspaces | Download |
| 3 | Lecture 03 : Linear Span and Linear Dependence | Download |
| 4 | Lecture 04 : Linear Independence | Download |
| 5 | Lecture 05 : Basis and Dimension | Download |
| 6 | Lecture 06 : Linear Functionals | Download |
| 7 | Lecture 07 : Norm of Vector Part - I | Download |
| 8 | Lecture 08 : Norm of Vector Part - II | Download |
| 9 | Lecture 09 : Linear Functions | Download |
| 10 | Lecture 10 : Affine Functions and Examples | Download |
| 11 | Lecture 11: Examples of Linear and Affine Functions | Download |
| 12 | Lecture 12: Function Composition | Download |
| 13 | Lecture 13: System of Linear Equations | Download |
| 14 | Lecture 14: Left Invertibility | Download |
| 15 | Lecture 15: Invertibility of Matrices | Download |
| 16 | Lecture 16: Triangular Systems | Download |
| 17 | Lecture 17: LU Decomposition Part - I | Download |
| 18 | Lecture 18: LU Decomposition Part - II | Download |
| 19 | Lecture 19: QR Decomposition (Rotators - Part I) | Download |
| 20 | Lecture 20: QR Decomposition (Rotators - Part II) | Download |
| 21 | Lecture 21: QR Decomposition (Reflectors - Part I) | Download |
| 22 | Lecture 22: QR Decomposition (Reflectors - Part II) | Download |
| 23 | Lecture 23: Matrix Norms | Download |
| 24 | Lecture 24: Sensitivity Analysis | Download |
| 25 | Lecture 25: Condition Number of a Matrix | Download |
| 26 | Lecture 26: Sensitivity Analysis -II | Download |
| 27 | Lecture 27: Sensitivity Analysis -III | Download |
| 28 | Lecture 28: Least Squares - Part I | Download |
| 29 | Lecture 29: Least Squares - Part II | Download |
| 30 | Lecture 30: Least Squares - Part III | Download |
| 31 | Lecture 31: Least Squares Data Fitting | Download |
| 32 | Lecture 32: Examples of LS data fitting | Download |
| 33 | Lecture 33: Classification using Least Squares | Download |
| 34 | Lecture 34: Examples of LS classification | Download |
| 35 | Lecture 35: Constrained Least Squares | Download |
| 36 | Lecture 36: Multiobjective Least Squares | Download |
| 37 | Lecture 37: Eigenvalues and Eigenvectors (Part - I) | Download |
| 38 | Lecture 38: Eigenvalues and Eigenvectors (Part - II) | Download |
| 39 | Lecture 39: Spectral Decomposition Theorem | Download |
| 40 | Lecture 40: Positive Definite Matrices | Download |
| 41 | Lecture 41 : Singular Value Decomposition (SVD) | Download |
| 42 | Lecture 42 : Proof of SVD | Download |
| 43 | Lecture 43 : Properties of SVD | Download |
| 44 | Lecture 44 : Another Proof of SVD | Download |
| 45 | Lecture 45 : Low Rank Approximations | Download |
| 46 | Lecture 46 : Principal Component Analysis | Download |
| 47 | Lecture 47 : SVD and Pseudo - Inverse | Download |
| 48 | Lecture 48 : SVD and the Least Squares Problem | Download |
| 49 | Lecture 49 : Sensitivity Analysis of the Least Squares Problem | Download |
| 50 | Lecture 50 : Power Method | Download |
| 51 | Lecture 51 : Directed Graphs and Properties | Download |
| 52 | Lecture 52 : Page Ranking Algorithm | Download |
| 53 | Lecture 53 : Inverse Eigen Value Problem | Download |
| 54 | Lecture 54 : Fastest Mixing Markov Chains on Graphs (Part I) | Download |
| 55 | Lecture 55 : Fastest Mixing Markov Chains on Graphs (Part II) | Download |
| 56 | Lecture 56: Sparse Solution and Underdetermined Systems | Download |
| 57 | Lecture 57: Structured Low Rank Approximations (Part I) | Download |
| 58 | Lecture 58: Structured Low Rank Approximations (Part II) | Download |
| 59 | Lecture 59: Structured Low Rank Approximations (Part III) | Download |
| 60 | Lecture 60: Recap | Download |
| Sl.No | Chapter Name | English |
|---|---|---|
| 1 | Lecture 01 : Vector Spaces | PDF unavailable |
| 2 | Lecture 02 : Vector Subspaces | PDF unavailable |
| 3 | Lecture 03 : Linear Span and Linear Dependence | PDF unavailable |
| 4 | Lecture 04 : Linear Independence | PDF unavailable |
| 5 | Lecture 05 : Basis and Dimension | PDF unavailable |
| 6 | Lecture 06 : Linear Functionals | PDF unavailable |
| 7 | Lecture 07 : Norm of Vector Part - I | PDF unavailable |
| 8 | Lecture 08 : Norm of Vector Part - II | PDF unavailable |
| 9 | Lecture 09 : Linear Functions | PDF unavailable |
| 10 | Lecture 10 : Affine Functions and Examples | PDF unavailable |
| 11 | Lecture 11: Examples of Linear and Affine Functions | PDF unavailable |
| 12 | Lecture 12: Function Composition | PDF unavailable |
| 13 | Lecture 13: System of Linear Equations | PDF unavailable |
| 14 | Lecture 14: Left Invertibility | PDF unavailable |
| 15 | Lecture 15: Invertibility of Matrices | PDF unavailable |
| 16 | Lecture 16: Triangular Systems | PDF unavailable |
| 17 | Lecture 17: LU Decomposition Part - I | PDF unavailable |
| 18 | Lecture 18: LU Decomposition Part - II | PDF unavailable |
| 19 | Lecture 19: QR Decomposition (Rotators - Part I) | PDF unavailable |
| 20 | Lecture 20: QR Decomposition (Rotators - Part II) | PDF unavailable |
| 21 | Lecture 21: QR Decomposition (Reflectors - Part I) | PDF unavailable |
| 22 | Lecture 22: QR Decomposition (Reflectors - Part II) | PDF unavailable |
| 23 | Lecture 23: Matrix Norms | PDF unavailable |
| 24 | Lecture 24: Sensitivity Analysis | PDF unavailable |
| 25 | Lecture 25: Condition Number of a Matrix | PDF unavailable |
| 26 | Lecture 26: Sensitivity Analysis -II | PDF unavailable |
| 27 | Lecture 27: Sensitivity Analysis -III | PDF unavailable |
| 28 | Lecture 28: Least Squares - Part I | PDF unavailable |
| 29 | Lecture 29: Least Squares - Part II | PDF unavailable |
| 30 | Lecture 30: Least Squares - Part III | PDF unavailable |
| 31 | Lecture 31: Least Squares Data Fitting | PDF unavailable |
| 32 | Lecture 32: Examples of LS data fitting | PDF unavailable |
| 33 | Lecture 33: Classification using Least Squares | PDF unavailable |
| 34 | Lecture 34: Examples of LS classification | PDF unavailable |
| 35 | Lecture 35: Constrained Least Squares | PDF unavailable |
| 36 | Lecture 36: Multiobjective Least Squares | PDF unavailable |
| 37 | Lecture 37: Eigenvalues and Eigenvectors (Part - I) | PDF unavailable |
| 38 | Lecture 38: Eigenvalues and Eigenvectors (Part - II) | PDF unavailable |
| 39 | Lecture 39: Spectral Decomposition Theorem | PDF unavailable |
| 40 | Lecture 40: Positive Definite Matrices | PDF unavailable |
| 41 | Lecture 41 : Singular Value Decomposition (SVD) | PDF unavailable |
| 42 | Lecture 42 : Proof of SVD | PDF unavailable |
| 43 | Lecture 43 : Properties of SVD | PDF unavailable |
| 44 | Lecture 44 : Another Proof of SVD | PDF unavailable |
| 45 | Lecture 45 : Low Rank Approximations | PDF unavailable |
| 46 | Lecture 46 : Principal Component Analysis | PDF unavailable |
| 47 | Lecture 47 : SVD and Pseudo - Inverse | PDF unavailable |
| 48 | Lecture 48 : SVD and the Least Squares Problem | PDF unavailable |
| 49 | Lecture 49 : Sensitivity Analysis of the Least Squares Problem | PDF unavailable |
| 50 | Lecture 50 : Power Method | PDF unavailable |
| 51 | Lecture 51 : Directed Graphs and Properties | PDF unavailable |
| 52 | Lecture 52 : Page Ranking Algorithm | PDF unavailable |
| 53 | Lecture 53 : Inverse Eigen Value Problem | PDF unavailable |
| 54 | Lecture 54 : Fastest Mixing Markov Chains on Graphs (Part I) | PDF unavailable |
| 55 | Lecture 55 : Fastest Mixing Markov Chains on Graphs (Part II) | PDF unavailable |
| 56 | Lecture 56: Sparse Solution and Underdetermined Systems | PDF unavailable |
| 57 | Lecture 57: Structured Low Rank Approximations (Part I) | PDF unavailable |
| 58 | Lecture 58: Structured Low Rank Approximations (Part II) | PDF unavailable |
| 59 | Lecture 59: Structured Low Rank Approximations (Part III) | PDF unavailable |
| 60 | Lecture 60: Recap | PDF unavailable |
| Sl.No | Language | Book link |
|---|---|---|
| 1 | English | Not Available |
| 2 | Bengali | Not Available |
| 3 | Gujarati | Not Available |
| 4 | Hindi | Not Available |
| 5 | Kannada | Not Available |
| 6 | Malayalam | Not Available |
| 7 | Marathi | Not Available |
| 8 | Tamil | Not Available |
| 9 | Telugu | Not Available |