Statements (60)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:academic
|
| gptkbp:fieldOfStudy |
gptkb:machine_learning
|
| gptkbp:focusesOn |
gptkb:information_theory
gptkb:probability_theory gptkb:curse_of_dimensionality gptkb:PAC_learning gptkb:reinforcement_learning gptkb:kernel_methods gptkb:no_free_lunch_theorem gptkb:empirical_risk_minimization gptkb:VC_dimension neural networks online learning optimization gradient descent statistical inference supervised learning active learning bias-variance tradeoff computational complexity cross-validation decision trees ensemble methods feature selection overfitting regularization semi-supervised learning support vector machines underfitting unsupervised learning model selection computational statistics bagging boosting generalization sample efficiency convex optimization probabilistic models structural risk minimization data complexity learning curves learning algorithms model complexity deep learning theory sample distribution loss functions non-convex optimization Bayesian learning theory algorithmic foundations algorithmic stability convergence guarantees learning bounds risk minimization sample complexity |
| https://www.w3.org/2000/01/rdf-schema#label |
Theory of Machine Learning
|
| gptkbp:relatedTo |
gptkb:artificial_intelligence
learning theory computational learning theory |
| gptkbp:bfsParent |
gptkb:Max_Planck_Institute_for_Intelligent_Systems
|
| gptkbp:bfsLayer |
3
|