• Logistic regression is a supervised machine learning classifier that extracts real-valued features from the input, multiplies each by a weight, sums them, and passes the sum through a sigmoid function to generate a probability. A threshold is used to make a decision. • Logistic regression can be used with two classes (e.g., positive and negative sentiment) or with multiple classes (multinomial logistic regression, for example for n-ary text classification, part-of-speech labeling, etc.). • Multinomial logistic regression uses the softmax function to compute probabilities. • The weights (vector w and bias b) are learned from a labeled training set via a loss function, such as the cross-entropy loss, that must be minimized. • Minimizing this loss function is a convex optimization problem, and iterative algorithms like gradient descent are used to find the optimal weights. • Regularization is used to avoid overfitting.