Deep Learning for Biomedical Data

2 credits - Spring Term

Deep learning is a type of machine learning method that employs many layers of data representations to capture the characteristics of the input data at different levels. It is inspired by the organization of neurons in organisms and has shown superior performance in image classification, natural language processing, and predicting of gene functions. In this class, we will introduce the basic concepts of deep neural networks and GPU computing, discuss convolutional neural networks and recurrent neural networks structures, and examine a few biomedical applications. Students are expected to be familiar with linear algebra and machine learning and will participate in a group deep learning project.

CROSS-REGISTRATION INFORMATION

Students outside of HMS who wish to cross-register into this course must submit a cross-reg petition in my.harvard. HSPH students should enroll in the cross-listed version of the course, EPI 290. Cross-reg petitions will be reviewed and approved on a first-come first-served basis beginning January 18, 2022. 

Jointly Offered with:

Harvard Chan School as EPI 290

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Assistant Professor of Epidemiology, Harvard T.H. Chan School of Public Health
Assistant Professor of Biomedical Informatics, Harvard Medical School (Secondary)