Loading...

Course Description

Computational linear algebra offers a toolkit for solving high-dimensional problems across various fields, including machine learning, physics, and big data analytics. This course focuses on applied techniques, going beyond theory to teach practical methods such as LU and QR factorizations, least squares optimization, and principal component analysis (PCA).

You will engage directly with data-driven challenges, learning to compute efficiently, analyze complex datasets, and uncover actionable patterns that inform decisions in dynamic environments. By the end of this course, you’ll have the tools to approach computational problems with clarity and confidence in real-world applications.

Faculty Author

David Bindel; Anil Damle

Benefits to the Learner

  • Manipulate triangular and orthogonal matrices and articulate their properties
  • Deconstruct matrices using LU and QR factorizations and apply them to solve linear systems in computational contexts
  • Compute eigenvalues and eigenvectors of matrices and analyze their applications in stability and dynamics problems
  • Apply least squares methods and conduct principal component analysis (PCA) to simplify high-dimensional data structures

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

Loading...
Enroll Now - Select a section to enroll in
Type
2 week
Dates
Oct 07, 2026 to Oct 20, 2026
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Dec 30, 2026 to Jan 12, 2027
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Mar 24, 2027 to Apr 06, 2027
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Type
2 week
Dates
Jun 16, 2027 to Jun 29, 2027
Total Number of Hours
20.0
Course Fee(s)
Standard Price $999.00
Required fields are indicated by .