ML & AI in Genomics

Graduate-level course in machine learning and artificial intelligence methods applied to genomics. Covers hidden Markov models, machine learning approaches for classification and regression, convolutional neural networks, self-supervised language models, and their application to genome annotation, genome-wide association studies, and predicting the molecular and clinical consequences of genetic variation. Prior coursework in linear algebra, probability and statistics, and experience with programming and scientific computing using Python are strongly recommended. Familiarity with basic biology and genomics is helpful but not required.