Machine Learning: Features and Model Selection

Related lectures (32)

Page 3 of 4

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Explores over-learning, generalization, and under-learning in machine learning models.

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

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

Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.