Statistical and AI-Powered Methods for High-Dimensional Genomics Data Analysis

Catalog description

This course will introduce both statistical methods and modern AI techniques for analyzing high-dimensional genomics data. Students will engage with a range of statistical methodologies, including Bayesian inference, hierarchical models, and non-negative matrix factorization, alongside advanced AI tools such as deep learning, machine learning, and ensemble methods. The curriculum covers applications such as differential expression analysis, gene set enrichment analysis, and integration of multi-omics data sets. Practical sessions include data preprocessing, model selection, interpretation of genomic data, and visualization of results. Prerequisites include coursework in statistics, proficiency in R or Python, and a basic understanding of genomics.