Generalization Theory

Related lectures (31)

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Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Explores generalization in deep learning, covering model complexity, implicit bias, and the double descent phenomenon.

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Covers the design and optimization of neural network models in deep learning.

Covers generalization, overfitting, and model complexity in machine learning.

Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.