Lectures related to LED Drivers: Microcontrollers Understanding

Least Squares Solutions

Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.

QR Factorization: Least Squares System Resolution

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Predicting Rainfall: Miniproject BIO-322

Introduces a miniproject where students predict rainfall in Pully using machine learning, focusing on reproducibility and code quality.

Linear Algebra Review: Vector Spaces and Matrix Operations

Offers a quick review of key linear algebra concepts essential for further topics.

Matrix Equivalence Theorems

Explores matrix equivalence theorems for systems of equations and least squares solutions.

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Least Squares Solutions

Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Matrix Factorization: Least Squares Method

Covers the factorization of a matrix and the least squares method.

Linear Dependence Theorems and Proofs

Explores linear dependence theorems and proofs, emphasizing the importance of understanding linear dependence in linear algebra.

Gram-Schmidt Algorithm: Orthogonalization and QR Factorization

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

Linear Models: Least Squares and QR Factorization

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

Homogeneous Solutions: Linear Independence

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

Linear Regression: Least Squares and Normal Equations

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

Linear Regression and Logistic Regression

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

Linear Transformations: Polynomials and Bases

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

PCA: Directions of Largest Variance

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

Linear Algebra: Linear Dependence and Independence

Explores linear dependence and independence of vectors in geometric spaces.

Coordinate Systems and Applications

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

Linear Regression: Statistical Inference and Regularization

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