Introduction to Machine Learning: Supervised Learning

This lecture covers the fundamentals of supervised learning, including the concepts of classification and regression. It delves into the training and testing stages of supervised learning, explaining how models are optimized and predictions are made. The lecture also explores linear models, overfitting, and regularization techniques. Additionally, it discusses the challenges of handling multiple output dimensions and classes, as well as the importance of adding non-linearity to models. The lecture concludes with an overview of kernel methods, support vector machines, and decision boundaries in classification.

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