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.
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이 강좌에 대하여
We recommend a solid foundation in one of the fields adjacent to the topic of the course, such as Computer Science, Data Science, Medicine.
귀하가 습득할 기술
- Graphs
- Unsupervised Learning
- Autoencoder
- Deep Learning
We recommend a solid foundation in one of the fields adjacent to the topic of the course, such as Computer Science, Data Science, Medicine.
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일리노이대학교 어버너-섐페인캠퍼스
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
석사 학위 취득 시작
강의 계획표 - 이 강좌에서 배울 내용
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
Deep Learning for Healthcare 특화 과정 정보
This specialization is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios.

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