Learn · Applied Linear Algebra in AI and ML
Lecture 57 of 60week-12
Lecture 57: Structured Low Rank Approximations (Part I)
Prof. Swanand Khare · IIT Kharagpur
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All the lectures
- Lecture 01 : Vector Spaces
- Lecture 02 : Vector Subspaces
- Lecture 03 : Linear Span and Linear Dependence
- Lecture 04 : Linear Independence
- Lecture 05 : Basis and Dimension
- Lecture 06 : Linear Functionals
- Lecture 07 : Norm of Vector Part - I
- Lecture 08 : Norm of Vector Part - II
- Lecture 09 : Linear Functions
- Lecture 10 : Affine Functions and Examples
- Lecture 11: Examples of Linear and Affine Functions
- Lecture 12: Function Composition
- Lecture 13: System of Linear Equations
- Lecture 14: Left Invertibility
- Lecture 15: Invertibility of Matrices
- Lecture 16: Triangular Systems
- Lecture 17: LU Decomposition Part - I
- Lecture 18: LU Decomposition Part - II
- Lecture 19: QR Decomposition (Rotators - Part I)
- Lecture 20: QR Decomposition (Rotators - Part II)
- Lecture 21: QR Decomposition (Reflectors - Part I)
- Lecture 22: QR Decomposition (Reflectors - Part II)
- Lecture 23: Matrix Norms
- Lecture 24: Sensitivity Analysis
- Lecture 25: Condition Number of a Matrix
- Lecture 26: Sensitivity Analysis -II
- Lecture 28: Least Squares - Part I
- Lecture 29: Least Squares - Part II
- Lecture 30: Least Squares - Part III
- Lecture 31: Least Squares Data Fitting
- Lecture 32: Examples of LS data fitting
- Lecture 33: Classification using Least Squares
- Lecture 34: Examples of LS classification
- Lecture 35: Constrained Least Squares
- Lecture 36: Multiobjective Least Squares
- Lecture 37: Eigenvalues and Eigenvectors (Part - I)
- Lecture 38: Eigenvalues and Eigenvectors (Part - II)
- Lecture 39: Spectral Decomposition Theorem
- Lecture 40: Positive Definite Matrices
- Lecture 27: Sensitivity Analysis -III
- Lecture 41 : Singular Value Decomposition (SVD)
- Lecture 42 : Proof of SVD
- Lecture 43 : Properties of SVD
- Lecture 44 : Another Proof of SVD
- Lecture 45 : Low Rank Approximations
- Lecture 46 : Principal Component Analysis
- Lecture 47 : SVD and Pseudo - Inverse
- Lecture 48 : SVD and the Least Squares Problem
- Lecture 49 : Sensitivity Analysis of the Least Squares Problem
- Lecture 50 : Power Method
- Lecture 51 : Directed Graphs and Properties
- Lecture 52 : Page Ranking Algorithm
- Lecture 53 : Inverse Eigen Value Problem
- Lecture 54 : Fastest Mixing Markov Chains on Graphs (Part I)
- Lecture 55 : Fastest Mixing Markov Chains on Graphs (Part II)
- Lecture 56: Sparse Solution and Underdetermined Systems
- Lecture 57: Structured Low Rank Approximations (Part I)
- Lecture 58: Structured Low Rank Approximations (Part II)
- Lecture 59: Structured Low Rank Approximations (Part III)
- Lecture 60: Recap