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Matrix Methods

13,792 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.1

249 reviews

Intermediate level
Some related experience required
7 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.1

249 reviews

Intermediate level
Some related experience required
7 hours to complete
Flexible schedule
Learn at your own pace

There are 5 modules in this course

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.

What's included

3 videos2 readings3 assignments

3 videos•Total 11 minutes
  • Matrix: Tabular Data•3 minutes
  • Matrix Multiplication•5 minutes
  • Supplement: Matrices in Python/Numpy•2 minutes
2 readings•Total 40 minutes
  • Vector and Matrix operations•30 minutes
  • Matrix Multiplication•10 minutes
3 assignments•Total 30 minutes
  • Matrix•30 minutes
  • Linear combinations•0 minutes
  • Matrix Combinations•0 minutes

What's included

3 videos2 readings3 assignments

3 videos•Total 14 minutes
  • Matrix as Mathematical Objects•4 minutes
  • Matrix Transpose•5 minutes
  • Supplement: Matrix Transpose in Python•5 minutes
2 readings•Total 20 minutes
  • Matrix Arithmetic•10 minutes
  • Matrix Transpose•10 minutes
3 assignments•Total 60 minutes
  • Matrix Operations•30 minutes
  • Matrix Transpose•30 minutes
  • Matrix Multiplication and Other Operations•0 minutes

What's included

4 videos3 readings4 assignments

4 videos•Total 15 minutes
  • Systems of Linear Equations•4 minutes
  • Solution of Linear Equations via Elimination•3 minutes
  • LU Decomposition: Matrix is a Product of Simple Matrices•6 minutes
  • Supplement: Solve Linear Equations in Python•3 minutes
3 readings•Total 30 minutes
  • Systems of Linear Equations•10 minutes
  • Gaussian Elimination Algorithm•10 minutes
  • LU Decomposition•10 minutes
4 assignments
  • Systems of Linear Equations•0 minutes
  • Solution of Linear Equations via Elimination•0 minutes
  • LU Decomposition•0 minutes
  • Systems of linear equations•0 minutes

What's included

4 videos2 readings5 assignments

4 videos•Total 16 minutes
  • Orthogonality and Inner Product.•4 minutes
  • Linear Least Squares: Best Approximation•3 minutes
  • Least Distance -> Orthogonality -> Normal Equations•5 minutes
  • Example: Approximate Curve Fitting•5 minutes
2 readings•Total 20 minutes
  • Orthogonality and the Inner Product•10 minutes
  • Linear Least Squares•10 minutes
5 assignments•Total 60 minutes
  • Orthogonality and Inner Product•0 minutes
  • Linear Least Squares•0 minutes
  • Normal equations•30 minutes
  • Approximate Curve Fitting•30 minutes
  • Linear Least Squares•0 minutes

What's included

2 readings3 assignments

2 readings•Total 20 minutes
  • S V D•10 minutes
  • Latent Semantic Indexing•10 minutes
3 assignments•Total 60 minutes
  • SVD as a Decomposition•30 minutes
  • SVD as a Data Analytics Tool•30 minutes
  • Singular Value Decomposition•0 minutes

Instructor

Instructor ratings
3.8 (54 ratings)
University of Minnesota
1 Course•13,792 learners

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Showing 3 of 249

JD
·

Reviewed on May 31, 2020

Very good course, the questions are really challenging...

DB
·

Reviewed on Nov 17, 2019

Pros and cons.Sometimes it's hard to find in this course needed information to solve Assignments.But you have to dig deeper from outside sources.

AV
·

Reviewed on Aug 31, 2022

This has been a helpful course. I had the chance to learn about practical applications of matrices.

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