BIOENG 2390 - ARTIFICIAL INTELLIGENCE APPLICATIONS IN BIOENGINEERING
Minimum Credits: 3
Maximum Credits: 3
Artificial Intelligence Applications in Bioengineering is a hands-on exploration of linear to non-linear modeling paradigms for biomedical data. In this course students will learn about aspects of information processing including data pre-processing, visualization, regression, dimensionality reduction (PCA, ICA), feature selection, classification (logistic regression, SVM, neural networks, etc.) and their usage for decision support in the context of healthcare. The course will provide an overview of the basics of scientific computing on local and cloud-based resources (i.e. relevant DevOps and MLOps) and scientific programming fundamentals, in addition to covering machine learning techniques for classification as well as regression modeling from biomedical datasets in tabular forms, including time series data. Students will additionally learn how to address classification and regression modeling tasks starting with 2D/3D medical imaging data. The course is designed to be practical with computer based tutorials and assignments on machine learning in general including popular kernelized models and deep neural networks.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad Letter Grade
Click here for class schedule information.
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