Data Representations & Processing

Related lectures (30)

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Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Covers overfitting, model selection, validation, cross-validation, regularization, kernel regression, and data representation challenges.

Covers data representations and processing techniques essential for effective machine learning algorithms.

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.