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EECE.5160 Biomedical Imaging and Data Science

Id: 041116 Credits Min: 3 Credits Max: 3


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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