Prototyping with SP80 - MAKE project

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Related lectures (32)

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Prediction tests

Explores out-of-sample validation and the methodology of cross-validation for testing predictive models.

Nonlinear ML Algorithms

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

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.

Overfitting, Cross-validation, Regularization

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

Image Classification: Overfitting and Accuracy Measures

Explores overfitting and accuracy measures in image classification, emphasizing the importance of model generalization and optimal accuracy.

Linear Regression and Gradient Descent

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Overfitting, Cross-validation & Regularization

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

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

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

Probabilistic Linear Regression

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

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