LED Drivers: Microcontrollers Understanding

Related lectures (26)

Page 2 of 3

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.

Covers least squares, QR factorization, linear models, and regression analysis with applications to experimental data.

Explores finding particular solutions for homogeneous differential equations, emphasizing linear independence and variation of constants.

Explores linear regression through least squares and normal equations, emphasizing the importance of minimizing errors for accurate predictions.

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Covers linear transformations between polynomial spaces and explores examples of linear independence and bases.

Covers PCA, finding directions of largest variance, data dimensionality reduction, and limitations of PCA.

Explores linear dependence and independence of vectors in geometric spaces.

Covers the definition and use of coordinate systems and applications in bases and linear equations.

Covers the probabilistic model for linear regression and the importance of regularization techniques.