University of Minnesota, Twin Cities - Matrix Methods
- Offered byCoursera
- Public/Government Institute
Matrix Methods at Coursera Overview
Duration | 7 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Matrix Methods at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level
- Approx. 7 hours to complete
- English Subtitles: French, Portuguese (European), Russian, English, Spanish
Matrix Methods at Coursera Course details
- Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
Matrix Methods at Coursera Curriculum
Matrices as Mathematical Objects
Matrix: Tabular Data
Matrix Multiplication
Supplement: Matrices in Python/Numpy
Vector and Matrix operations
Matrix Multiplication
Matrix
Linear combinations
Matrix Combinations
Matrix Multiplication and other Operations
Matrix as Mathematical Objects
Matrix Transpose
Supplement: Matrix Transpose in Python
Matrix Arithmetic
Matrix Transpose
Matrix Operations
Matrix Transpose
Matrix Multiplication and Other Operations
Systems of Linear Equations
Systems of Linear Equations
Solution of Linear Equations via Elimination
LU Decomposition: Matrix is a Product of Simple Matrices
Supplement: Solve Linear Equations in Python
Systems of Linear Equations
Gaussian Elimination Algorithm
LU Decomposition
Systems of Linear Equations
Solution of Linear Equations via Elimination
LU Decomposition
Systems of linear equations
Linear Least Squares
Orthogonality and Inner Product.
Linear Least Squares: Best Approximation
Least Distance -> Orthogonality -> Normal Equations
Example: Approximate Curve Fitting
Orthogonality and the Inner Product
Linear Least Squares
Orthogonality and Inner Product
Linear Least Squares
Normal equations
Approximate Curve Fitting
Linear Least Squares
Singular Value Decomposition
S V D
Latent Semantic Indexing
SVD as a Decomposition
SVD as a Data Analytics Tool
Singular Value Decomposition
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