Machine Learning and Privacy

Related lectures (31)

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

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

Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

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

Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

Explores underfitting, overfitting, and the bias-variance trade-off in machine learning models.

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.