Concepts in Linear Algebra. One of the most interesting topics to visualize in Linear Algebra are Eigenvectors and Eigenvalues. Here you will learn how to easily calculate them and how they are applicable and particularly interesting when it comes to machine learning implementations.
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In the computational world of AI you will often encounter enormous amounts of data that needs to be processed. Often, the data volume will be so large that you will need to use some form of data reduction technique. Eigen-concepts are a big part of the mathematical background needed to understand a useful data reduction tools, calledPrincipal Component Analysis (PCA).
What you will learn
Vectors
- Linear Transformation
Definitions and Calculations
- Characteristic Equation of a matrix
- Eigenvalues
- Eigenvectors
Why is the relevant to Machine Learning?
- Principle Component Analysis (PCA)
Prerequisites and requirements
To easily understand this class you will need to have mathematical background in Linear Algebra. Refresh your memory or go over the topics of Linear transformation , Determinants And a System of linear equations before beginning.
