Specification Testing and Machine Learning

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Related lectures (30)

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Model Complexity and Overfitting in Machine Learning

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Nonlinear ML Algorithms

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

Model Assessment and Hyperparameter Tuning

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Machine Learning Fundamentals: Regularization and Cross-validation

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

Cross-validation & Regularization

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

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Cross-Validation: Techniques and Applications

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Overfitting, Cross-validation, Regularization

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