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Deep Learning Methods for Healthcare
University of Illinois at Urbana-Champaign via Coursera
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Overview
The “Deep Learning Methods for Healthcare” course provides a detailed introduction to the use of deep learning techniques in addressing critical healthcare challenges. Targeted at data scientists, healthcare professionals, and researchers, this course explores how neural networks can be applied to improve medical diagnosis, patient monitoring, and treatment planning. Participants will learn foundational deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders, with practical applications in medical imaging, electronic health records (EHR), and biomedical...
Syllabus
- Week 1 - Embedding
- An overview of the course and everything about Embedding.
- Week 2 - Convolutional Neural Networks (CNN)
- We discuss the importance of Convolution and Pooling, and then present relevant information about Convolutional Neural Networks.
- Week 3 - Recurrent Neural Networks (RNN)
- Recurrent Neural Network have important building blocks. We'll explain those and give examples for healthcare applications.
- Week 4 - Autoencoders
- Learn why Autoencoders are indispensible in Machine Learning. We'll also show you how this is applied in healthcare.
University of Illinois at Urbana-Champaign via Coursera
22 hours
Paid Certificate Available
English
On-Demand
Advanced
Instructor
Jimeng Sun
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