Machine Learning Foundations: Linear Algebra
Offered By: LinkedIn Learning
Course Description
Overview
Explore the fundamentals of linear algebra, the mathematical foundation of machine learning algorithms.
Syllabus
Introduction
- Introduction
- What you should know
- Defining linear algebra
- Applications of linear algebra in ML
- Introduction to vectors
- Vector arithmetic
- Coordinate system
- Dot product of vectors
- Scalar and vector projection
- Changing basis of vectors
- Basis, linear independence, and span
- Matrices introduction
- Types of matrices
- Types of matrix transformation
- Composition or combination of matrix transformations
- Solving linear equations using Gaussian elimination
- Gaussian elimination and finding the inverse matrix
- Inverse and determinant
- Matrices changing basis
- Transforming to the new basis
- Orthogonal matrix
- Gram–Schmidt process
- Introduction to eigenvalues and eigenvectors
- Calculating eigenvalues and eigenvectors
- Changing to the eigenbasis
- Google PageRank algorithm
- Next steps
Taught by
Terezija Semenski
Related Courses
Coding the Matrix: Linear Algebra through Computer Science ApplicationsBrown University via Coursera Mathematical Methods for Quantitative Finance
University of Washington via Coursera Introduction à la théorie de Galois
École normale supérieure via Coursera Linear Algebra - Foundations to Frontiers
The University of Texas at Austin via edX Massively Multivariable Open Online Calculus Course
Ohio State University via Coursera