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Linear Algebra Level 3 (8 Sessions)

Linear algebra is central to almost all areas of mathematics. Linear algebra is also used in most sciences and fields of engineering, because it allows modeling many natural phenomena, and computing efficiently with such models. 

In this course, we will teach the key concepts Linear Algebra, following the curriculum in Linear Algebra Done Right textbook, together using practical example via open source software such as Python Numpy Linear Algebra library and Octave.

Course Schedule

  • No of Sessions: 8 (4 weeks)
  • Duration Per Session : 1.5 hrs (7pm to 8:30pm)
  • Start on beginning of every month, Monday, Tuesday
  • Platform: Zoom (preferred) or Face-to-Face
  • Class size: 1 to 5 pax
  • Age Group: 16 years old and abo

    If you are keen, you can register your interest first. We will send you the payment link once the class is confirmed.

    Course Code: K867c

    Course Booking

    $800.00 (GST-exclusive)
    $872.00 (GST-inclusive)

    Course Date

    Course Time

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    Post-Course Support

    We provide free consultation related to the subject matter after the course. Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts and get back to you asap.

    Course Cancellation/Reschedule Policy

    We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
    Note the venue of the training is subject to changes due to class size and availability of the classroom.
    Note the minimal class size to start a class is 3 Pax.


    Course Details

    Lesson 1: Orthonormal Bases
    Lesson 2: Minimisation Problems
    Lesson 3: Adjoints
    Lesson 4: Self-Adjoint Operators
    Lesson 5: The Spectral Theorem
    Lesson 6: Postive Operators
    Lesson 7: Isometrics
    Lesson 8: Polar Decomposition
    Lesson 9: Singular Value Decomposition
    Lesson 10: Null Spaces of Powers of an Operator
    Lesson 11: Generalised Eigenvectors
    Lesson 12: Nilpotent Operators
    Lesson 13: Decomposition via Generalised Eigenvectors
    Lesson 14: Block Diagonal Matrices
    Lesson 15: Square Roots of Operators
    Lesson 16: The Characteristics Polynomials
    Lesson 17: The Minimal Polynomials

     

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