Lectures related to Linear Regression: Maximum Likelihood Approach

Covers the basics of exploratory statistics, including variables, quantiles, central tendency, dispersion, outliers, and robustness.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

Covers optimal errors, phase transitions, and algorithm performance in linear models.