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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Courses and Certificate Fees

Fees InformationsCertificate AvailabilityCertificate Providing Authority
INR 1000yesIIT Bombay

The Syllabus

  • Lecture 1: Why should we care about algebra?
  • Lecture 2: Uses of linear algebra in different domains
  • Lecture 3: Power of abstraction and geometric insights
  • Lecture 4: Equivalent systems of linear equations
  • Lecture 5: Row reduced form
  • Lecture 6: Row reduced echelon form

  • Lecture 7: Solving for Ax=0
  • Lecture 8: Row rank of matrices
  • Lecture 9: Groups and Abelian Groups
  • Lecture 10: Rings, integral domains, and fields
  • Lecture 11: Fields: Examples and properties
  • Lecture 12: Vector Spaces

  • Lecture 13: Examples of vector spaces
  • Lecture 14: Subspaces
  • Lecture 15: Examples of subspaces
  • Lecture 16: Sum and intersection of subspaces
  • Lecture 17: Span and linear independence
  • Lecture 18: Generating set and basis

  • Lecture 19: Properties of basis
  • Lecture 20: Dimension of a vector space
  • Lecture 21: Dimensions of special subspaces and properties
  • Lecture 22: Co-ordinates and ordered basis
  • Lecture 23: Row and column rank
  • Lecture 24: Rank and nullity of matrices

  • Lecture 25: Linear transformations and operators
  • Lecture 26: Rank nullity theorem for linear transformations
  • Lecture 27: Injective, surjective and bijective linear mappings
  • Lecture 28: Isomorphism and their compositions
  • Lecture 29: Linear transformations under change of basis
  • Lecture 30: Linear functionals

  • Lecture 31: Dual basis and dual maps
  • Lecture 32: Annihilators, double duals
  • Lecture 33: Products of vector spaces
  • Lecture 34: Quotient spaces
  • Lecture 35: Quotient maps
  • Lecture 36: First isomorphism theorem

  • Lecture 37: Inner product spaces
  • Lecture 38: Examples of inner products
  • Lecture 39: Cauchy Schwarz and triangle inequalities
  • Lecture 40: Some results and applications of inner products (in solving Ax=b)
  • Lecture 41: Gram-Schmidt orthonormalization
  • Lecture 42: Best approximation of a vector in a subspace

  • Lecture 43: Orthogonal complements of subspaces and their properties
  • Lecture 44: Orthogonal projection map and its properties
  • Lecture 45: “Best” solution for Ax=b
  • Lecture 46: Applications of “best” solution
  • Lecture 47: Adjoint operators on inner product spaces
  • Lecture 48: Miscellaneous results on inner products and inner product spaces, and their applications (e.g. Haar wavelets, Fourier series)

  • Lecture 49: Solutions of linear second order differential equations and phase portraits
  • Lecture 50: Eigenvalues and eigen vectors
  • Lecture 51: Diagonalizability for self-adjoint operators
  • Lecture 52: Linear independence of eigen vectors and diagonalizability, evaluation of matrix functions
  • Lecture 53: Algebraic and geometric multiplicities
  • Lecture 54: Decomposition of a vector space into sums and direct sums of suitable subspaces

  • Lecture 55: Equivalent conditions for diagonalizability
  • Lecture 56: A-invariant subspaces: definition and examples
  • Lecture 57: Polynomials and their ideals
  • Lecture 58: Minimal polynomial
  • Lecture 59: Minimal polynomial and characteristic polynomial
  • Lecture 60: Further properties of minimal polynomial

  • Lecture 61: Bezout’s identity for polynomials
  • Lecture 62: Application of Bezout’s identity to coprime factors of minimal polynomial
  • Lecture 63: Recipe for best representation of non-diagonalizable linear operators
  • Lecture 64: Jordan canonical form
  • Lecture 65: Proof for Jordan canonical form
  • Lecture 66: Proof of Cayley Hamilton theorem

  • Lecture 67: Application of linear algebra to algebraic graph theory
  • Lecture 68: Properties of graph Laplacian matrix: Fiedler eigenvalue
  • Lecture 69: Consensus problem
  • Lecture 70: Solution of the agreement protocol
  • Lecture 71: Applications to opinion dynamics
  • Lecture 72: Further applications of linear algebra to multi-agent systems

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