Math 2.0

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Applied Linear Algebra in AI and ML 3WA4E50FB0

URIm2://res/3WA4E50FB0
Kindcourse
CreatorsSwanand Khare (instructor)
Date2022-12-16
MSC15-XX
Scopein
Idscourse 03w5sq511:111105165

Expressions

e1: edition

Languageen
Date2022-12-16
Contents
  • part 1: week-01
  • session 1: Lecture 01 : Vector Spaces
  • session 2: Lecture 02 : Vector Subspaces
  • session 3: Lecture 03 : Linear Span and Linear Dependence
  • session 4: Lecture 04 : Linear Independence
  • session 5: Lecture 05 : Basis and Dimension
  • part 2: week-02
  • session 6: Lecture 06 : Linear Functionals
  • session 7: Lecture 07 : Norm of Vector Part - I
  • session 8: Lecture 08 : Norm of Vector Part - II
  • session 9: Lecture 09 : Linear Functions
  • session 10: Lecture 10 : Affine Functions and Examples
  • part 3: week-03
  • session 11: Lecture 11: Examples of Linear and Affine Functions
  • session 12: Lecture 12: Function Composition
  • session 13: Lecture 13: System of Linear Equations
  • session 14: Lecture 14: Left Invertibility
  • session 15: Lecture 15: Invertibility of Matrices
  • part 4: week-04
  • session 16: Lecture 16: Triangular Systems
  • session 17: Lecture 17: LU Decomposition Part - I
  • session 18: Lecture 18: LU Decomposition Part - II
  • session 19: Lecture 19: QR Decomposition (Rotators - Part I)
  • session 20: Lecture 20: QR Decomposition (Rotators - Part II)
  • part 5: week-05
  • session 21: Lecture 21: QR Decomposition (Reflectors - Part I)
  • session 22: Lecture 22: QR Decomposition (Reflectors - Part II)
  • session 23: Lecture 23: Matrix Norms
  • session 24: Lecture 24: Sensitivity Analysis
  • session 25: Lecture 25: Condition Number of a Matrix
  • part 6: week-06
  • session 26: Lecture 26: Sensitivity Analysis -II
  • session 27: Lecture 28: Least Squares - Part I
  • session 28: Lecture 29: Least Squares - Part II
  • session 29: Lecture 30: Least Squares - Part III
  • part 7: week-07
  • session 30: Lecture 31: Least Squares Data Fitting
  • session 31: Lecture 32: Examples of LS data fitting
  • session 32: Lecture 33: Classification using Least Squares
  • session 33: Lecture 34: Examples of LS classification
  • session 34: Lecture 35: Constrained Least Squares
  • part 8: week-08
  • session 35: Lecture 36: Multiobjective Least Squares
  • session 36: Lecture 37: Eigenvalues and Eigenvectors (Part - I)
  • session 37: Lecture 38: Eigenvalues and Eigenvectors (Part - II)
  • session 38: Lecture 39: Spectral Decomposition Theorem
  • session 39: Lecture 40: Positive Definite Matrices
  • part 9: week-09
  • session 40: Lecture 27: Sensitivity Analysis -III
  • session 41: Lecture 41 : Singular Value Decomposition (SVD)
  • session 42: Lecture 42 : Proof of SVD
  • session 43: Lecture 43 : Properties of SVD
  • session 44: Lecture 44 : Another Proof of SVD
  • session 45: Lecture 45 : Low Rank Approximations
  • part 10: week-10
  • session 46: Lecture 46 : Principal Component Analysis
  • session 47: Lecture 47 : SVD and Pseudo - Inverse
  • session 48: Lecture 48 : SVD and the Least Squares Problem
  • session 49: Lecture 49 : Sensitivity Analysis of the Least Squares Problem
  • session 50: Lecture 50 : Power Method
  • part 11: week-11
  • session 51: Lecture 51 : Directed Graphs and Properties
  • session 52: Lecture 52 : Page Ranking Algorithm
  • session 53: Lecture 53 : Inverse Eigen Value Problem
  • session 54: Lecture 54 : Fastest Mixing Markov Chains on Graphs (Part I)
  • session 55: Lecture 55 : Fastest Mixing Markov Chains on Graphs (Part II)
  • part 12: week-12
  • session 56: Lecture 56: Sparse Solution and Underdetermined Systems
  • session 57: Lecture 57: Structured Low Rank Approximations (Part I)
  • session 58: Lecture 58: Structured Low Rank Approximations (Part II)
  • session 59: Lecture 59: Structured Low Rank Approximations (Part III)
  • session 60: Lecture 60: Recap

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