Machine Learning for Medicine: Insights and Interpretability

Related lectures (31)

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Explores the Random Feature Method for solving PDEs using machine learning algorithms to approximate high-dimensional functions efficiently.

Explores adversarial training optimization, practical implementation, interpretability, fairness, Wasserstein distance, and Wasserstein GANs.

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Explores machine learning through solvable models, covering sample complexity, neural networks, and computational gaps.

Explores the extremes of interpretability in machine learning, focusing on sparse decision trees and interpretable neural networks.

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Delves into machine learned interatomic potentials, showcasing their accuracy and cost-effectiveness in predicting chemical properties.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Explores neural networks convergence through parameter fitting and alternating regret, with a focus on the NeurIPS 2023 event at EPFL.

Explores machine learning applications in atomistic simulations, focusing on water modeling, neural network potentials, and structure recognition.