This course covers deep learning (DL) methods, healthcare data and applications using DL methods. The courses include activities such as video lectures, self guided programming labs, homework assignments (both written and programming), and a large project.
The first phase of the course will include video lectures on different DL and health applications topics, self-guided labs and multiple homework assignments. In this phase, you will build up your knowledge and experience in developing practical deep learning models on healthcare data. The second phase of the course will be a large project that can lead to a technical report and functioning demo of the deep learning models for addressing some specific healthcare problems. We expect the best projects can potentially lead to scientific publications.
Advanced Deep Learning Methods for Healthcare
University of Illinois at Urbana-Champaign via Coursera
Overview
Syllabus
- Week 1 - Attention Models
- Attention Models are useful to detect specific features in a data source. We'll explain how it can be applied to the risk of heart failure.
- Week 2 - Graph Neural Networks
- In this week we'll explain the fundamentals of Graph Neural Networks.
- Week 3 - Memory Networks
- We'll explain the principles behind Memory Networks and how they can be used for predictions in medical applications.
- Week 4 - Generative Models
- We'll discuss Generative Networks, as well as the method of Variational Autoencoder
Taught by
Jimeng Sun
