Decision Trees and BoostingExplores decision trees in machine learning, their flexibility, impurity criteria, and introduces boosting methods like Adaboost.
Ensemble Methods: Random ForestExplores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.
Decision Trees and BoostingIntroduces decision trees as a method for machine learning and explains boosting techniques for combining predictors.
Multiclass ClassificationCovers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Statistical Physics of LearningOffers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
Nonlinear Supervised LearningExplores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Signal RepresentationsExplores wavelets as orthonormal bases for piecewise constant signals over unit intervals.
