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Machine Learning is a computational approach for construction of algorithms that can learn from and make predictions on data. The focus of the course is to deliver a practical approach that can help appropriate utilization of machine learning methods for data exploration and prediction tasks in biomedical applications. Applications will be drawn from bioinformatics, neuro-engineering, and biomedical image analysis, with special emphasis given to feature extraction and representation strategies specific to the data types prevalent in these domains. The machine learning concepts and methods will include parameter density estimation, dimension reduction, supervised and unsupervised learning, neural networks, and support vector machines.

College/Department: School of Biomedical Engineering, Science Health Systems
Repeat Status: Not repeatable for credit
Prerequisites: BMES 546 [Min Grade: B] or BMES 550 [Min Grade: B]