EECE.5160 — Graduate

Id: 041116 Offering: 1 Credits: 3-3

Description

An introduction to machine learning and signal processing for medical imaging and big data analytics. Overview of medical image reconstruction, registration, denoising, deblurring, and segmentation. Machine learning: supervised vs. unsupervised methods, training, testing, and cross-validation. Statistical estimation: least squares, maximum likelihood, and Bayesian methods. Regularization, overfitting and underfitting, and bias-variance trade-off. Numerical optimization: gradient descent, preconditioning, stochastic gradient descent. Clustering and classification. Deep learning: multilayer perceptrons, convolutional neural networks, recurrent nerural networks, autoencoders, and reinforcement learning. Deep learning software suites. Application of data science tools to medical datasets.

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EECE.5160 — Online and Continuing Education

Id: 041116 Offering: 2 Credits: 3-3

Description

An introduction to machine learning and signal processing for medical imaging and big data analytics. Overview of medical image reconstruction, registration, denoising, deblurring, and segmentation. Machine learning: supervised vs. unsupervised methods, training, testing, and cross-validation. Statistical estimation: least squares, maximum likelihood, and Bayesian methods. Regularization, overfitting and underfitting, and bias-variance trade-off. Numerical optimization: gradient descent, preconditioning, stochastic gradient descent. Clustering and classification. Deep learning: multilayer perceptrons, convolutional neural networks, recurrent nerural networks, autoencoders, and reinforcement learning. Deep learning software suites. Application of data science tools to medical datasets.

Prerequisites

Students with a CSCE or UGRD career need permission to take Graduate Level Courses.

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