Robust Optimization in Radiation Therapy

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Related lectures (30)

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Robust Optimization: Radiation Therapy & Support Vector Machines

Explores robust optimization in radiation therapy and support vector machines.

Quantile Regression: Linear Optimization

Covers quantile regression, focusing on linear optimization for predicting outputs and discussing sensitivity to outliers, problem formulation, and practical implementation.

Optimization Methods: Theory Discussion

Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Optimization Principles

Covers optimization principles, including linear optimization, networks, and concrete research examples in transportation.

Gradient Descent

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Polynomial Regression and Gradient Descent

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

L1 Regularization: Sparse Solutions and Dimensionality Reduction

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.