Dimensionality Reduction: Curse of Dimensionality

This lecture covers the curse of dimensionality, which implies that in high dimensions, data points become increasingly isolated, requiring more training data. Methods for variable selection, such as filtering based on correlation or mutual information, are discussed. The coefficient of determination is introduced as a measure of how well predicted values correlate with actual values. Limitations of filtering methods are highlighted, using the example of explaining the output of an exclusive OR (XOR) operation. Overall, the lecture emphasizes the challenges and strategies for reducing dimensionality in machine learning.