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Overview
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data.
Discover the Singular Value Decomposition with this Matrix Methods course offered by Coursera in partnership with University of Minnesota 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.
Skills you'll gain
- Mathematics
- Linear Algebra
- Problem Solving
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Programme Structure
Courses include:
- Matrices as Mathematical Objects
- Matrix Multiplication and other Operations
- Systems of Linear Equations
- Linear Least Squares
- Singular Value Decomposition
Key information
Duration
- Part-time
- 1 months
- 2 hrs/week
Start dates & application deadlines
You can apply for and start this programme anytime.
Language
English
Delivered
Online
Campus Location
- Mountain View, United States
Disciplines
Mathematics Machine Learning View 213 other Short Courses in Machine Learning in United StatesWhat students do after studying
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Academic requirements
We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.
English requirements
We are not aware of any English requirements for this programme.
Other requirements
General requirements
- Intermediate level
- Some related experience required
Tuition Fees
Tuition fees are shown in and the most likely applicable fee is shown based on your nationality.
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International Applies to you
Applies to youNon-residentsFree - Out-of-StateFree
Additional Details
- Coursera Plus: Subscribe to build job-ready skills from world-class institutions.
- $59/month, cancel anytime or $399/year with 14-day money-back guarantee
Funding
Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You’ll be prompted to complete an application and will be notified if you are approved.
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