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Related lectures (32)

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Hydraulic Transients of Turbines: Hydroacoustic Modeling

Explores hydraulic turbine modeling, stability, and historical development, emphasizing the selection criteria for Francis turbines.

Playing Connect Four

Explores the implementation of Connect Four game functionalities and testing methods.

Training and Testing Games

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

Model Complexity and Overfitting in Machine Learning

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

Software Engineering: Best Practices

Covers tools and best practices for software writing, debugging, testing, and managing code.

Model Evaluation: K-Nearest Neighbor

Explores model evaluation with K-Nearest Neighbor, covering optimal k selection, similarity metrics, and performance metrics for classification models.

Model Evaluation

Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

Simple validation, cross-validation, leave-one-out

Introduces techniques for obtaining unbiased risk estimates of learned predictors and their application for hyperparameter tuning.

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.

Linear and Ridge Regression

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.