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Linear Algebra

  1. Vectors
    1. Scalars
    2. Vectors vs Sets
    3. Addition and Subtraction
    4. Scalar Multiplication
    5. Zero Vectors
    6. Linear Combinations
    7. Real Dot Product
    8. Length of a Vector
    9. Orthogonal Vectors
    10. Parallel Vectors
    11. Angle Between Vectors
    12. Unit Vectors
  2. Matrices
    1. Notation
    2. Indexing
    3. Submatrices
    4. Matrix-by-Vector Product
    5. Addition and Subtraction
    6. Scalar Multiplication
    7. Transpose
    8. Symmetries
    9. Matrix Multiplication
    10. Identity Matrix
    11. Non-Negative Integer Powers
    12. Reverse Order Law of Transposition
  3. Linear Systems
    1. Inverse Matrices
    2. Singular Matrices
    3. Linear Dependence
    4. Solutions
  4. Planes
    1. Vector Cross Product
  5. Gaussian Elimination
Linear Algebra

Linear Algebra - Free University-Level Course

In mathematics, the field of Linear Algebra concerns itself with structures and systems that behave linearly.

It studies matrices and vectors, and their operations. It is possible to stretch, rotate, and combine vectors through different operations.

Linear Algebra has a vast range of applications, for example in the field of Computer Science. In Computer Science, Linear Algebra is used extensively for Neural Networks (think of LLMs like - most notably - OpenAI’s ChatGPT) and Computer Graphics.

The knowledge acquired in this course will help you in developing your own Machine Learning or Graphics library from scratch, if you so desire.

Other than in Computer Science, many other Engineering fields use Linear Algebra extensively, but as a Computer Scientist myself, I’m most familiar with its applications with regard to computers.

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Vectors - Introduction
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