Lectures related to Untitled | EPFL Graph Search

Delves into the complexity and interdependencies of transitioning to smart cities, emphasizing the importance of a holistic approach.

Explores the Bias-Variance tradeoff in machine learning, emphasizing the balance between bias and variance in model predictions.

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

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

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.