Logistic Regression
Logistic regression models the probabilities for classification problems with two possible outcomes. It’s an extension of the linear regression model for classification problems. In this topic you will get to know why we use the logistic regression over the linear regression.
https://christophm.github.io/interpretable-ml-book/logistic.html
In this topic you will get to know the derivation of logistic regression model.
http://www.haija.org/derivation_logistic_regression.pdf
ROC-AUC Curve/Score
An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. In this topic you will get to know the theory behind the ROC-AUC Curve and how the scores get calculated.
https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc
This topic will cover in video which is good for understanding using the visualization.
https://www.youtube.com/watch?v=xugjARegisk