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Jimeng Sun
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/sz9cr3/advanced
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What's inside
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.
Read more
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.
Read more
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
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores deep learning methods, healthcare data, and applications using deep learning methods, which is relevant in industry and academia
Features multiple activities such as video lectures, self-guided programming labs, homework assignments, and projects
Provides opportunities to build practical deep learning models on healthcare data in the first phase of the course
Culminates in a final project that may lead to the publication of technical reports and demos
Introduces Attention Models and their applications in healthcare
Provides an understanding of Graph Neural Networks
Covers Memory Networks and their applications in predicting medical outcomes
Examines different Generative Models, including Variational Autoencoder
Save this course
Save Advanced Deep Learning Methods for Healthcare to your list so you can find it easily later:
Save
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Advanced Deep Learning Methods for Healthcare with these
activities:
Find a mentor who can provide guidance on deep learning for healthcare
Show steps
Finding a mentor can provide you with valuable guidance and support as you learn about deep learning for healthcare.
Show steps
-
Identify someone who has experience in deep learning for healthcare.
-
Reach out to the person and ask if they would be willing to mentor you.
Review Linear Algebra Concepts
Show steps
Refresh foundational knowledge in linear algebra, which is essential for understanding deep learning algorithms and their application in healthcare.
Browse courses on
Linear Algebra
Show steps
-
Review lecture notes or textbooks on linear algebra concepts such as vectors, matrices, and transformations.
-
Solve practice problems to reinforce understanding.
-
Complete online tutorials or interactive exercises on linear algebra.
Follow a tutorial on how to use a deep learning library
Show steps
Using a deep learning library will make it easier for you to develop and train deep learning models.
Show steps
-
Choose a deep learning library to learn.
-
Find a tutorial on how to use the library.
-
Follow the tutorial and try to implement the examples.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Follow Tutorials on Deep Learning Healthcare Tools
Show steps
Gain proficiency with deep learning tools and frameworks commonly used in healthcare applications by completing guided tutorials and exercises.
Browse courses on
TensorFlow
Show steps
-
Identify relevant tutorials on platforms such as Coursera, edX, or GitHub.
-
Follow the tutorials step-by-step, building models and experimenting with different techniques.
-
Experiment with hyperparameter tuning and model optimization.
Collaborate on a Healthcare Deep Learning Project
Show steps
Work with peers to develop a deep learning model for a specific healthcare problem, fostering teamwork and knowledge sharing.
Show steps
-
Form a team with complementary skills and interests.
-
Brainstorm ideas and select a healthcare problem to address.
-
Design and implement a deep learning solution using appropriate techniques.
-
Validate the model's performance and prepare a presentation for sharing.
Practice building and training deep learning models
Show steps
Building and training deep learning models will help you to develop the skills that you need to succeed in this course.
Browse courses on
Deep Learning Models
Show steps
-
Find a dataset that you can use to train a deep learning model.
-
Build a deep learning model for the dataset.
-
Train the deep learning model.
-
Evaluate the performance of the deep learning model.
Practice Deep Learning Algorithms
Show steps
Solve practice problems to improve understanding and proficiency in deep learning algorithms for healthcare applications.
Browse courses on
Deep Learning
Show steps
-
Identify a relevant dataset from the course resources or online sources.
-
Choose a deep learning algorithm suitable for the task, such as CNNs or RNNs.
-
Develop a model architecture and train it on the selected dataset.
-
Evaluate the model's performance and make adjustments as needed.
Analyze Real-World Healthcare Data
Show steps
Develop analytical skills by working with real-world healthcare data, enhancing understanding of data patterns and their relevance in healthcare applications.
Show steps
-
Obtain access to a relevant healthcare dataset from public sources or research institutions.
-
Explore the data using data visualization tools and statistical techniques.
-
Identify trends, patterns, and relationships within the data.
-
Interpret the findings and discuss their implications for healthcare.
Volunteer with a healthcare organization that uses deep learning
Show steps
Volunteering with a healthcare organization will give you the opportunity to apply your knowledge of deep learning to real-world problems and make a difference in the lives of others.
Browse courses on
Healthcare
Show steps
-
Identify a healthcare organization that uses deep learning.
-
Contact the organization and ask about volunteer opportunities.
Write a Blog Post on a Deep Learning Healthcare Application
Show steps
Communicate knowledge and insights gained from the course by creating a blog post that explores a specific deep learning application in healthcare, reinforcing understanding.
Browse courses on
Deep Learning Applications
Show steps
-
Research and gather information on a chosen healthcare application of deep learning.
-
Organize and outline the content of the blog post.
-
Write a clear and engaging post, explaining the application and its significance.
-
Proofread and publish the blog post on a relevant platform.
Contribute to an Open-Source Healthcare Deep Learning Project
Show steps
Engage with the broader deep learning community and contribute to ongoing research by participating in an open-source project related to healthcare applications.
Show steps
-
Identify an active open-source healthcare deep learning project.
-
Review the project's documentation and identify potential areas for contribution.
-
Contact the project maintainers to discuss and propose contributions.
-
Submit code changes, documentation updates, or other contributions according to project guidelines.
Develop a deep learning model for a healthcare problem
Show steps
Developing a deep learning model for a healthcare problem will help you to apply the skills that you have learned in this course to a real-world problem.
Browse courses on
Deep Learning Models
Show steps
-
Identify a healthcare problem that you would like to solve with a deep learning model.
-
Collect data that you can use to train the deep learning model.
-
Build and train a deep learning model for the healthcare problem.
-
Evaluate the performance of the deep learning model.
Write a technical report on deep learning for healthcare
Show steps
Writing a technical report will help you to summarize the knowledge that you have gained in this course and demonstrate your understanding of deep learning for healthcare.
Browse courses on
Deep Learning
Show steps
-
Choose a topic for your technical report.
-
Research the topic.
-
Write the technical report.
Participate in a deep learning competition
Show steps
Participating in a deep learning competition will allow you to test your skills against other students and learn from the best in the field.
Browse courses on
Deep Learning
Show steps
-
Find a deep learning competition that you would like to participate in.
-
Join a team or start your own team.
-
Develop a deep learning model for the competition.
-
Submit your model to the competition.
Find a mentor who can provide guidance on deep learning for healthcare
Show steps
Finding a mentor can provide you with valuable guidance and support as you learn about deep learning for healthcare.
Show steps
Show steps
Show steps
- Identify someone who has experience in deep learning for healthcare.
- Reach out to the person and ask if they would be willing to mentor you.
Review Linear Algebra Concepts
Show steps
Refresh foundational knowledge in linear algebra, which is essential for understanding deep learning algorithms and their application in healthcare.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on linear algebra concepts such as vectors, matrices, and transformations.
- Solve practice problems to reinforce understanding.
- Complete online tutorials or interactive exercises on linear algebra.
Follow a tutorial on how to use a deep learning library
Show steps
Using a deep learning library will make it easier for you to develop and train deep learning models.
Show steps
Show steps
Show steps
- Choose a deep learning library to learn.
- Find a tutorial on how to use the library.
- Follow the tutorial and try to implement the examples.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Follow Tutorials on Deep Learning Healthcare Tools
Show steps
Gain proficiency with deep learning tools and frameworks commonly used in healthcare applications by completing guided tutorials and exercises.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Identify relevant tutorials on platforms such as Coursera, edX, or GitHub.
- Follow the tutorials step-by-step, building models and experimenting with different techniques.
- Experiment with hyperparameter tuning and model optimization.
Collaborate on a Healthcare Deep Learning Project
Show steps
Work with peers to develop a deep learning model for a specific healthcare problem, fostering teamwork and knowledge sharing.
Show steps
Show steps
Show steps
- Form a team with complementary skills and interests.
- Brainstorm ideas and select a healthcare problem to address.
- Design and implement a deep learning solution using appropriate techniques.
- Validate the model's performance and prepare a presentation for sharing.
Practice building and training deep learning models
Show steps
Building and training deep learning models will help you to develop the skills that you need to succeed in this course.
Browse courses on
Deep Learning Models
Show steps
Show steps
Show steps
- Find a dataset that you can use to train a deep learning model.
- Build a deep learning model for the dataset.
- Train the deep learning model.
- Evaluate the performance of the deep learning model.
Practice Deep Learning Algorithms
Show steps
Solve practice problems to improve understanding and proficiency in deep learning algorithms for healthcare applications.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify a relevant dataset from the course resources or online sources.
- Choose a deep learning algorithm suitable for the task, such as CNNs or RNNs.
- Develop a model architecture and train it on the selected dataset.
- Evaluate the model's performance and make adjustments as needed.
Analyze Real-World Healthcare Data
Show steps
Develop analytical skills by working with real-world healthcare data, enhancing understanding of data patterns and their relevance in healthcare applications.
Show steps
Show steps
Show steps
- Obtain access to a relevant healthcare dataset from public sources or research institutions.
- Explore the data using data visualization tools and statistical techniques.
- Identify trends, patterns, and relationships within the data.
- Interpret the findings and discuss their implications for healthcare.
Volunteer with a healthcare organization that uses deep learning
Show steps
Volunteering with a healthcare organization will give you the opportunity to apply your knowledge of deep learning to real-world problems and make a difference in the lives of others.
Browse courses on
Healthcare
Show steps
Show steps
Show steps
- Identify a healthcare organization that uses deep learning.
- Contact the organization and ask about volunteer opportunities.
Write a Blog Post on a Deep Learning Healthcare Application
Show steps
Communicate knowledge and insights gained from the course by creating a blog post that explores a specific deep learning application in healthcare, reinforcing understanding.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Research and gather information on a chosen healthcare application of deep learning.
- Organize and outline the content of the blog post.
- Write a clear and engaging post, explaining the application and its significance.
- Proofread and publish the blog post on a relevant platform.
Contribute to an Open-Source Healthcare Deep Learning Project
Show steps
Engage with the broader deep learning community and contribute to ongoing research by participating in an open-source project related to healthcare applications.
Show steps
Show steps
Show steps
- Identify an active open-source healthcare deep learning project.
- Review the project's documentation and identify potential areas for contribution.
- Contact the project maintainers to discuss and propose contributions.
- Submit code changes, documentation updates, or other contributions according to project guidelines.
Develop a deep learning model for a healthcare problem
Show steps
Developing a deep learning model for a healthcare problem will help you to apply the skills that you have learned in this course to a real-world problem.
Browse courses on
Deep Learning Models
Show steps
Show steps
Show steps
- Identify a healthcare problem that you would like to solve with a deep learning model.
- Collect data that you can use to train the deep learning model.
- Build and train a deep learning model for the healthcare problem.
- Evaluate the performance of the deep learning model.
Write a technical report on deep learning for healthcare
Show steps
Writing a technical report will help you to summarize the knowledge that you have gained in this course and demonstrate your understanding of deep learning for healthcare.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Choose a topic for your technical report.
- Research the topic.
- Write the technical report.
Participate in a deep learning competition
Show steps
Participating in a deep learning competition will allow you to test your skills against other students and learn from the best in the field.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a deep learning competition that you would like to participate in.
- Join a team or start your own team.
- Develop a deep learning model for the competition.
- Submit your model to the competition.
Career center
Learners who complete Advanced Deep Learning Methods for Healthcare will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use advanced analytical techniques to extract meaningful insights from data. As a Data Scientist, you would use deep learning methods to develop accurate models for predicting patient outcomes, optimizing treatment plans, and detecting diseases early on. This course would provide you with the foundational knowledge and practical experience to excel in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. In healthcare, Machine Learning Engineers play a crucial role in building models for early disease detection, personalized treatment planning, and drug discovery. This course would equip you with the deep learning expertise necessary to succeed in this field.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use data to improve healthcare delivery and outcomes. They analyze data to identify trends, patterns, and opportunities for improvement. Deep learning methods are increasingly used by Healthcare Analysts to extract valuable insights from vast amounts of healthcare data. This course would provide you with the skills to leverage deep learning for better healthcare decision-making.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to solve problems in healthcare. They design studies, analyze data, and interpret results to inform healthcare decisions. Deep learning methods are becoming increasingly important in biostatistics, as they allow for the analysis of complex data sets. This course would provide you with the deep learning skills needed to thrive in this field.
Medical Physicist
Medical Physicist
Medical Physicists use physics principles to solve problems in medicine. They work in a variety of settings, including hospitals, clinics, and research institutions. Deep learning methods are increasingly used by Medical Physicists to develop new imaging techniques, optimize treatment plans, and improve patient safety. This course would provide you with the deep learning knowledge necessary to excel in this field.
Clinical Data Manager
Clinical Data Manager
Clinical Data Managers are responsible for managing clinical data in research studies. They ensure that data is accurate, complete, and compliant with regulations. Deep learning methods are increasingly used by Clinical Data Managers to improve data quality and efficiency. This course would provide you with the deep learning skills needed to succeed in this field.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants advise healthcare organizations on how to improve their operations and performance. They use their expertise in healthcare and business to help organizations solve problems, improve efficiency, and achieve their goals. Deep learning methods are increasingly used by Healthcare Consultants to analyze data, identify trends, and make recommendations. This course would provide you with the deep learning knowledge necessary to thrive in this field.
Pharmaceutical Scientist
Pharmaceutical Scientist
Pharmaceutical Scientists are responsible for developing new drugs and therapies. They work in a variety of settings, including pharmaceutical companies, research institutions, and government agencies. Deep learning methods are increasingly used by Pharmaceutical Scientists to identify new drug targets, optimize drug design, and predict clinical outcomes. This course would provide you with the deep learning skills needed to succeed in this field.
Health Informatics Specialist
Health Informatics Specialist
Health Informatics Specialists use technology to improve healthcare delivery and outcomes. They work in a variety of settings, including hospitals, clinics, and insurance companies. Deep learning methods are increasingly used by Health Informatics Specialists to analyze data, identify trends, and develop new tools and technologies. This course would provide you with the deep learning skills needed to succeed in this field.
Medical Writer
Medical Writer
Medical Writers create written materials about medical topics for a variety of audiences. They work in a variety of settings, including pharmaceutical companies, medical device companies, and healthcare organizations. Deep learning methods are increasingly used by Medical Writers to analyze data, identify trends, and develop new educational materials. This course would provide you with the deep learning skills needed to succeed in this field.
Nursing Informatics Specialist
Nursing Informatics Specialist
Nursing Informatics Specialists use technology to improve nursing practice and patient outcomes. They work in a variety of settings, including hospitals, clinics, and home health agencies. Deep learning methods are increasingly used by Nursing Informatics Specialists to analyze data, identify trends, and develop new tools and technologies. This course would provide you with the deep learning skills needed to succeed in this field.
Healthcare Technology Manager
Healthcare Technology Manager
Healthcare Technology Managers are responsible for planning, implementing, and managing healthcare technology systems. They work in a variety of settings, including hospitals, clinics, and insurance companies. Deep learning methods are increasingly used by Healthcare Technology Managers to analyze data, identify trends, and make recommendations. This course would provide you with the deep learning knowledge necessary to thrive in this field.
Medical Laboratory Scientist
Medical Laboratory Scientist
Medical Laboratory Scientists analyze body fluids and tissues to diagnose and treat diseases. They work in a variety of settings, including hospitals, clinics, and research institutions. Deep learning methods are increasingly used by Medical Laboratory Scientists to analyze data, identify trends, and develop new diagnostic tests. This course would provide you with the deep learning skills needed to succeed in this field.
Health Educator
Health Educator
Health Educators promote health and wellness in a variety of settings. They work with individuals, groups, and communities to develop and implement health education programs. Deep learning methods are increasingly used by Health Educators to analyze data, identify trends, and develop new educational materials. This course would provide you with the deep learning skills needed to succeed in this field.
Occupational Therapist
Occupational Therapist
Occupational Therapists help people with disabilities or injuries to improve their functional abilities. They work in a variety of settings, including hospitals, clinics, schools, and rehabilitation centers. Deep learning methods are increasingly used by Occupational Therapists to analyze data, identify trends, and develop new rehabilitative techniques. This course would provide you with the deep learning skills needed to succeed in this field.
For more career information including salaries, visit:
OpenCourser.com/course/sz9cr3/advanced
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Advanced Deep Learning Methods for Healthcare.
Provides a practical introduction to deep learning using Python, covering the fundamentals of deep learning models, architectures, and training techniques, making it a valuable resource for implementing deep learning models in healthcare applications.
Provides a practical guide to machine learning using Scikit-Learn and TensorFlow 2. It covers topics such as data preprocessing, feature engineering, model selection, and model evaluation.
Provides a conceptual and intuitive explanation of deep learning, making it a helpful resource for understanding the underlying principles and concepts of deep learning for healthcare.
Save
Provides a comprehensive overview of natural language processing using deep learning. It covers topics such as text classification, named entity recognition, and question answering.
Provides a comprehensive overview of deep learning for natural language processing, including text classification, machine translation, and question answering, making it a valuable resource for understanding the fundamentals and applications of deep learning in healthcare natural language processing tasks.
Provides a comprehensive overview of structural equation modeling, making it a valuable resource for understanding the fundamentals and applications of structural equation modeling in healthcare research.
Provides a comprehensive overview of deep reinforcement learning. It covers topics such as the theory of deep reinforcement learning, the different types of deep reinforcement learning algorithms, and the applications of deep reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/sz9cr3/advanced
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Effort
This course is designed for 4 weeks, but it can be stretched out by the learner as needed.
Level
Advanced
Via
Coursera
Institution
University of Illinois at Urbana-Champaign
Instructor
Jimeng Sun
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
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This course belongs to a
collection
called
Deep Learning for Healthcare. It's comprised of three courses:
- Health Data Science Foundation
- Deep Learning Methods for Healthcare
- Advanced Deep Learning Methods for Healthcare (viewing)
Good to know
Explores deep learning methods, healthcare data, and applications using deep learning methods, which is relevant in industry and academia
Features multiple activities such as video lectures, self-guided programming labs, homework assignments, and projects
Provides opportunities to build practical deep learning models on healthcare data in the first phase of the course
Culminates in a final project that may lead to the publication of technical reports and demos
Introduces Attention Models and their applications in healthcare
Provides an understanding of Graph Neural Networks
Covers Memory Networks and their applications in predicting medical outcomes
Examines different Generative Models, including Variational Autoencoder
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