CSE529: Computational Genomics
Description: Computational and statistical approaches and practices for deriving robust and rigorous insights from modern genomics datasets. Lectures alternate between genomics-inspired problem formulation and foundational statistical and computational approaches for addressing them. In foundational lectures, we will cover basics of statistical inference, hidden confounding factors, causality and causal inference, deep neural networks and interpretation approaches to deep learning models.
Prerequisities: (none listed) Credits: 4.0Portions of the CSE529 web may be reprinted or adapted for academic nonprofit purposes, providing the source is accurately quoted and duly creditied. The CSE529 Web: © 1993-2024, Department of Computer Science and Engineering, Univerity of Washington. Administrative information on CSE529 (authentication required).