Lecture
This lecture covers the basics of statistical learning, including different types of learning settings, supervised learning formalization, decision theoretic framework, risk minimization, and overfitting symptoms. It also delves into key concepts such as Tikhonov regularization, polynomial regression, dimensionality reduction, SVMs, neural networks, and ethical considerations in machine learning.
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