Machine Learning in Finance

Related lectures (31)

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Covers risk measures used for determining risk capital and capital adequacy.

Analyzes gradient descent on two-layer ReLU neural networks, exploring global convergence, regularization, implicit bias, and statistical efficiency.

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Explores the principles of finance regarding risk and return in investment decisions.

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

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

Delves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.