BMI 707 - Deep Learning for Biomedical Data (2 credit course, offered in spring)

Faculty: Andrew Beam, Nils Gehlenborg, Kun-Hsing Yu

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Deep learning is a type of machine learning methods that employed 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.


Spring 2 Semester 2018 

Meeting Times: Wednesday 2PM-5PM
Meeting Dates: 

  • Week 8- 3/21                                   
  • Week 9- 3/28          
  • Week 10- 4/4                                    
  • Week 11- 4/11                                    
  • Week 12- 4/18                                  
  • Week 13- 4/25                                    
  • Week 14- 5/2