Model Selection and Evaluation

Related lectures (32)

Page 3 of 4

Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Explores model complexity, overfitting, and the role of cross-validation and regularization in machine learning.

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

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

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Covers loading datasets, understanding dimensions, learning curves, and the impact of regularization on overfitting.

Explores Principal Component Analysis for dimension reduction and data representation in a new basis.