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Related lectures (30)
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Statistical Physics of Learning
Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.
AdaBoost: Decision Stumps
Explores AdaBoost with decision stumps, discussing error rules, stump selection, and the need for a bias term.
Threat Modeling: Adversarial Examples and Backdoors
Covers threat modeling in deep learning for autonomous vehicles, focusing on defending against adversarial examples and backdoors.
Randomized Classifiers in the ROC Plane
Explores how randomized classifiers achieve convex combinations in the ROC plane.
Entity & Information Extraction
Explores information extraction using classifiers, features, and syntactic analysis.
Learning in the Presence of Distribution Shifts: How does the Geometry of Perturbations Play a Role?
Explores learning challenges with distribution shifts and perturbation geometry, focusing on robust classifiers and natural variation modeling.
Decision Trees: Induction & Attributes
Explores decision trees, attribute selection, bias-variance tradeoff, and ensemble methods in machine learning.
Decision Trees: Overfitting and Randomization
Explores decision trees, overfitting, and randomization in supervised learning, emphasizing the importance of managing variance and feature selection.
Virtual World: Engineering Reality
Explores a unique world where you can engineer reality and create innovative tools.
Graph Neural Networks: Interconnected World
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
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