Topics will vary semester to semester. See the Computer Science Division announcements.

This special topics course will cover the computational, statistical and biological background required for students to understand recent research in the field of computational genomics. Each course topic will be motivated by one or more key research papers in the field. To understand these papers, we will cover computational methods including hidden Markov models, machine learning approaches for classification and regression, convolutional neural networks, and their application to genome annotation, genome-wide association studies, and predicting the molecular and clinical consequences of genetic variation. We will also cover relevant biological background on genome structure and function, epigenomics, and gene expression regulation. At the end of the class, students should feel comfortable reading research papers and understanding important open research questions in computational genomics.

* Time conflicts are NOT allowed for this class.

* Prerequisites: EECS 126 and Math 54 or equivalent. CS 189 and basic biology/genomics are helpful but not required.

Repeat Rules