Lectures related to Basics of Linear Regression

Explores applications of machine learning, biased datasets, statistical distributions, and software demos.

Explores the physical, chemical, and morphological analysis of powders, emphasizing sampling accuracy and quality control.

Covers the solution of linear equations and the linearity of functions.

Explores data representations, histograms, neural networks, and deep learning concepts.

Covers the implementation of Markov Chain Monte Carlo (MCMC) with the Metropolis algorithm for sampling from posterior distributions.