2 Logistic Regression.
2.1 Classification.
2.2 Hypothesis representation.
2.2.1 Interpreting hypothesis output.
2.3 Decision boundary.
2.3.1 Non-linear decision boundaries.
2.4 Cost function for logistic regression.
2.4.1 A convex logistic regression cost function.
2.5 Simplified cost function and gradient descent.
2.5.1 Probabilistic interpretation for cost function.
2.5.2 Gradient Descent for logistic regression.
2.6 Multiclass classification problem
key words: logistic regression, classification, decision boundary, convex function, One-vs-all
