51视频

EECE.5160 Biomedical Imaging and Data Science

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.

Course prerequisites/corequisites are determined by the faculty and approved by the curriculum committees. Students are required to fulfill these requirements prior to enrollment. For courses offered through online or GPS delivery, students are responsible for confirming with the instructor or department that all enrollment requirements have been satisfied before registering.