Machine Learning in Healthcare Course Syllabus Detailed

This document outlines a comprehensive curriculum for a Machine Learning in Healthcare course. It covers fundamental concepts like the distinction between machine learning and deep learning, various neural network architectures including Single-Layer Perceptrons and Multi-Layer Perceptrons, and optimization algorithms such as Batch Gradient Descent, SGD, and Adam. The material also delves into deep learning specifics like activation functions, backpropagation, regularization techniques, and Convolutional Neural Networks (CNNs), with a particular focus on their application in medical imaging using modalities like MRI and CT. Furthermore, the course addresses crucial aspects of medical AI, including dataset preparation, handling class imbalance, cross-validation strategies, and the importance of Explainable AI (XAI) and algorithmic bias in clinical decision-support systems. AI

RANK_REASON The item is a detailed syllabus for an academic course on machine learning in healthcare, covering theoretical concepts and practical applications. [lever_c_demoted from research: ic=1 ai=1.0]

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• Adam optimizer
• Batch Gradient Descent
• CNN
• computed tomography
• deep learning
• hyperbolic tangent
• machine learning
• magnetic resonance imaging
• Mini batch Gradient Descent
• multilayer perceptron
• rectifier
• RMSprop
• SGD
• sigmoid function
• Single-Layer Perceptron
• stochastic gradient descent

• paper
• other

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