Special Topics: Machine Learning Applications in Experimental BME Research

Catalog description

This course is designed to introduce students to applications of artificial intelligence and machine learning in experimental BME research. The course will be focused on the main data types that are generated: tabular data from sets of experiments, image data, spectral data, and time-series data. A diversity of regression and classification methods, including linear models, Gaussian processes, tree-based methods, and TabPFN, will be introduced with an emphasis on experimental design and modeling datasets based on small sample sizes. Important examples include quantitative analysis of cells in culture, in situ spectroscopic data, and omics data. Methods of statistical analysis, feature selection, and transfer learning will also be introduced.