Foundational lectures:
Statistical inference: hypothesis testing, group comparison, inference, and multiple testing
Confounding factors and observational datasets
Causality and instrumental variable analysis
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
Special-topic lectures: genomics of COVID-19 and prediction of disease severity
Exposure to a variety of cutting-edge research directions in Computational Genomics
Critically evaluate literature, and identify strengths and weakness of machine learning and statistical approaches in the context of biological discovery
Identify strategies to address research challenges, including design of baseline experiments, and model evaluation with limited "gold-standard" data
Student evaluations will be based on writing short paper critiques (3x15%), 1 final project (which can be done alone or in a group; 45%), and class participation (10%)
(recommended) an introductory class in Machine Learning at undergraduate level.
(recommended) an introductory course in computational biology, for example CSE427 or CSE527, or equivalent offered by other institutions. To fulfill this requirement student can also complete an online course including MIT's ML in genomics course that is freely available.
Competency in programming in python or a similar language. This will be required for implementing your course project.
ML
A Course in Machine Learning by Hal Daumé III: http://ciml.info/.
Pattern Recognition and Machine Learning by Chris Bishop. (pdf)
Deep Learning by Goodfellow, Bengio, and Courville (html)
Biology
Introduction to Molecular Biology (pdf).
A lengthy, general resource: Molecular Biology of the Cell v5 (pdf)
Lecture videos from other courses:
Deep learning in life sciences (by Prof Kellis, MIT): https://mit6874.github.io/
Suggested review articles:
Opportunities and obstacles for deep learning in biology and medicine (here)