Prediction tests

Related lectures (31)

Page 2 of 4

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

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.

Explores hydroacoustic simulation parameters, mass oscillation modeling, and validation errors in hydroelectric installations.

Emphasizes the importance of separating training and testing data for machine learning models.

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

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

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

Explores generalization, model selection, and validation in machine learning, emphasizing the importance of unbiased model evaluation.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.