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Explores generalization in machine learning, focusing on underfitting and overfitting trade-offs, teacher-student frameworks, and the impact of random features on model performance.
Explores advanced optimization techniques for machine learning models, focusing on adaptive gradient methods and their applications in non-convex optimization problems.
Explores Principal Component Analysis for dimensionality reduction in machine learning, showcasing its feature extraction and data preprocessing capabilities.
