Machine Learning for Medicine: Insights and Interpretability
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Delves into the application of artificial intelligence in finance, exploring tools like neural networks and Bayesian techniques, successful use cases in fraud detection and robo-advisors, and the importance of interpretability in machine learning models.
Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
Explores machine learning applications in Earth system analysis using remote sensing data, focusing on automatic image interpretation and explainable AI.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
Explores the concept of explainable neural networks and their significance in improving model interpretability, particularly in finance and house price valuation.
