Deep Learning Methods for Healthcare
Master advanced deep learning techniques for healthcare applications. Learn CNN, RNN, and autoencoders.
Master advanced deep learning techniques for healthcare applications. Learn CNN, RNN, and autoencoders.
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.
3.5
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What you'll learn
Master embedding techniques for healthcare data
Implement CNN architectures for medical applications
Develop RNN models for sequential medical data
Apply autoencoder variants in healthcare contexts
Create practical deep learning solutions for healthcare problems
Skills you'll gain
This course includes:
2.98 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores deep learning methods specifically applied to healthcare applications. Students learn advanced techniques including embedding methods for electronic health records, convolutional neural networks for medical imaging, recurrent neural networks for sequential medical data, and autoencoders for healthcare data analysis. The curriculum combines theoretical foundations with practical implementations through programming assignments and a significant project that could potentially lead to scientific publications.
Embedding
Module 1 · 5 Hours to complete
Convolutional Neural Networks (CNN)
Module 2 · 5 Hours to complete
Recurrent Neural Networks (RNN)
Module 3 · 8 Hours to complete
Autoencoders
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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3.5 course rating
11 ratings
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