Supervised Learning Essentials

This lecture covers the fundamentals of supervised learning, including topics such as logistic regression, linear classification, and likelihood maximization. It delves into the concepts of probability distributions, noise modeling, and the importance of choosing the right loss function. The presentation also includes practical examples and exercises to reinforce the theoretical concepts.