Foundational lectures:
Statistical inference: hypothesis testing, group comparison, inference, and multiple testing
Confounding factors and observational datasets
Machine learning principles, deep neural networks, and explainable AI techniques
Genomics lectures:
Human genome and genetics of disease
Gene expression and observational study designs
Multi-omic studies, goals, and challenges
Gene regulation and systematic studies of regulatory genomics
Single cell genomics problems
Student evaluations will be based on 3 assignments, and class participation.
(Strongly recommended) a class in applied statistics at undergraduate level.
(Strongly recommended) an introductory class in Machine Learning at undergraduate level.
Competency in programming in python or a similar language. This will be required for completing assignments.
ML
Pattern Recognition and Machine Learning by Chris Bishop. (pdf)
Deep Learning by Goodfellow, Bengio, and Courville (html)
Biology