Least Squares & Orthogonality

This minimizes the total area of the squares. Least Squares & Orthogonality lets you explore this hands-on instead of only reading about it.

Focuses on minimizing the residual norm and applying least squares to regression and classification. It focuses on Residual norm, Normal equations, Feature engineering, Overfitting, Least squares classifier from the “The Least Squares Problem and Data Fitting” section.

You can drag elements directly on the canvas; the “Reset Points”, “Add Random Point” buttons run actions or reset the demo. Every change updates the visualization in real time, so you can see exactly how each variable affects the outcome.

This course provides a practical introduction to linear algebra, focusing on the core concepts of vectors, matrices, and least squares methods. It emphasizes real-world applications such as machine learning, control sys… This demo is the interactive piece for the “The Least Squares Problem and Data Fitting” section. Open the full Applied Linear Algebra and Least Squares course wiki at https://www.getlykke.com/explore/public/applied-linear-algebra-and-least-squares-f2e383ce-d1e8-4a0a-af8a-f21feb45dad0 for notes, flashcards, quizzes and the other demos.

You'll build intuition for Residual norm, Normal equations, Feature engineering, Overfitting, Least squares classifier — and, crucially, see how they behave when you change the inputs, which is hard to get from a textbook or lecture on Applied Linear Algebra and Least Squares alone.