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Course Description

Linear algebra provides the foundation for describing computational systems and tackling numerical problems. It is a robust and systematic framework used to represent, optimize, and solve linear systems, forming the backbone of machine learning, optimization, and data science workflows.

In this course, you will cover essential topics such as matrix operations, fundamental subspaces, projections, and singular value decomposition (SVD). You’ll discover how to apply these tools to represent linear systems mathematically and computationally. By the end of the course, you’ll have the experience to use linear algebra techniques confidently in real-world applications requiring precision and optimization.

Faculty Author

David Bindel; Anil Damle

Benefits to the Learner

  • Use matrix properties and operations (addition, multiplication, transposition) and vector spaces to represent and manipulate linear systems
  • Derive and use compact or data-sparse matrix representations for efficient computation
  • Analyze and compute the four fundamental subspaces (column space, row space, null space, and left null space) and solve projection problems using orthogonal matrices
  • Execute singular value decomposition on matrices and interpret its role in dimensionality reduction and data compression

Target Audience

  • Software engineers building AI-powered applications
  • Data analysts and scientists working with large-scale datasets
  • Engineers applying computational methods to complex systems
  • Web and frontend developers integrating machine learning features
  • Computational biologists and scientific researchers modeling real-world phenomena
  • Investment managers leveraging quantitative analysis
  • Game developers optimizing physics engines and AI behaviors
  • Anyone in a technical role seeking to strengthen their mathematical foundation for AI and machine learning

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in
Type
2 week
Dates
Sep 23, 2026 to Oct 06, 2026
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Dec 16, 2026 to Dec 29, 2026
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Mar 10, 2027 to Mar 23, 2027
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Jun 02, 2027 to Jun 15, 2027
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
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