Careers360 Logo
ask-icon
share
    Compare

    Quick Facts

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

    Important dates

    Certificate Exam Date

    Start Date : 18 Apr, 2026

    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

    Instructors

    Articles

    Student Community: Where Questions Find Answers

    Ask and get expert answers on exams, counselling, admissions, careers, and study options.