Advanced Deep Learning Methods for Healthcare
Master advanced deep learning techniques for healthcare applications, from attention models to generative networks.
Master advanced deep learning techniques for healthcare applications, from attention models to generative networks.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Deep Learning for Healthcare Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Apply attention models to healthcare risk assessment
Implement graph neural networks for medical data analysis
Develop memory networks for clinical predictions
Create generative models for healthcare applications
Design end-to-end deep learning solutions for medical problems
Skills you'll gain
This course includes:
3.4 Hours PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on advanced deep learning methods specifically applied to healthcare challenges. The curriculum covers four main areas: attention models for heart failure risk assessment, graph neural networks for medical applications, memory networks for clinical predictions, and generative models including GANs and VAEs. Students engage in hands-on programming labs and projects that can potentially lead to scientific publications. The course combines theoretical foundations with practical implementation, preparing students for real-world healthcare AI applications.
Week 1 - Attention Models
Module 1 · 5 Hours to complete
Week 2 - Graph Neural Networks
Module 2 · 5 Hours to complete
Week 3 - Memory Networks
Module 3 · 3 Hours to complete
Week 4 - Generative Models
Module 4 · 2 Hours to complete
Fee Structure
Instructor
Professor of Computer Science
Jimeng Sun is a Professor in the Computer Science Department at the University of Illinois Urbana-Champaign (UIUC), where he specializes in applying artificial intelligence (AI) to healthcare. Before joining UIUC, he served as an Associate Professor at the Georgia Institute of Technology and was a researcher at IBM's TJ Watson Research Center. His research focuses on several critical areas, including deep learning for drug discovery, clinical trial optimization, computational phenotyping, clinical predictive modeling, treatment recommendations, and health monitoring.Dr. Sun has made significant contributions to the field, publishing over 120 papers and filing more than 20 patents, with five granted. He has been recognized as one of the Top 100 AI Leaders in Drug Discovery and Advanced Healthcare. His accolades include the SDM/IBM Early Career Research Award in 2017 and multiple best paper awards at prestigious conferences. Jimeng holds a B.S. and M.Phil. in Computer Science from the Hong Kong University of Science and Technology and a Ph.D. from Carnegie Mellon University. At UIUC, he teaches courses such as "Advanced Deep Learning Methods for Healthcare" and "Health Data Science Foundation," where he equips students with the skills necessary to leverage AI in improving healthcare outcomes.
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