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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/mx3fma/deep
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What's inside
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.
Read more
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.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches deep learning methods, which are crucial for healthcare applications
Provides hands-on experience through programming labs and homework assignments
Capstone project allows learners to apply their knowledge and skills to address real-world healthcare problems
Covers various deep learning techniques relevant to healthcare, including embedding, CNNs, RNNs, and autoencoders
Led by instructors with expertise in healthcare data and applications
Save this course
Save Deep Learning Methods for Healthcare to your list so you can find it easily later:
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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 Deep Learning Methods for Healthcare with these
activities:
Review fundamentals of Machine Learning
Show steps
Revisit the foundational concepts of machine learning to strengthen the understanding of deep learning methodologies.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Review lecture notes or textbooks on machine learning concepts.
-
Complete practice exercises or online quizzes to reinforce understanding.
Organize course notes and materials
Show steps
Consolidate and organize course materials to enhance understanding and facilitate review.
Show steps
-
Gather all lecture notes, assignments, quizzes, and exams.
-
Create a system for organizing the materials, such as folders or a digital notebook.
-
Review and annotate the materials to improve comprehension.
Learn about Deep Learning applications in healthcare
Show steps
Review materials on the practical applications of deep learning in healthcare to enhance understanding of the course content.
Show steps
-
Explore online resources, such as research papers or industry articles, that showcase deep learning applications in healthcare.
-
Identify specific examples of how deep learning is used to solve healthcare problems, such as disease diagnosis or drug discovery.
-
Analyze the benefits and challenges of using deep learning in healthcare.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a glossary of Deep Learning terms
Show steps
Compile a comprehensive glossary of key terms and concepts to enhance understanding and retention of the course content.
Show steps
-
Identify important terms and concepts from the course materials.
-
Define each term clearly and concisely.
-
Organize the terms alphabetically or by category.
Complete coding exercises on Healthcare Data
Show steps
Practice working with healthcare data to enhance understanding of the data handling techniques discussed in the course.
Browse courses on
Healthcare Data
Show steps
-
Find online platforms or tutorials that provide coding exercises on healthcare data.
-
Select exercises that involve data preprocessing, feature engineering, or model evaluation.
-
Implement the code using a programming language like Python or R.
-
Analyze the results and identify areas for improvement.
Consolidate knowledge of Convolutional Neural Networks (CNNs)
Show steps
Review materials on CNNs to reinforce understanding of the concepts discussed in Week 2.
Browse courses on
Convolutional Neural Networks
Show steps
-
Identify reliable online resources for CNN tutorials.
-
Follow video tutorials or read articles explaining the concepts of CNNs.
-
Work through the provided examples or exercises to practice implementing CNNs.
Engage in peer discussions on Autoencoders
Show steps
Collaborate with peers to explore and understand the concepts of Autoencoders covered in Week 4.
Browse courses on
Autoencoders
Show steps
-
Form study groups or join online forums dedicated to deep learning.
-
Choose a topic related to Autoencoders for discussion.
-
Prepare by reading research papers or reviewing course materials on the topic.
-
Engage in discussions, share insights, and ask questions to enhance understanding.
Solve practice problems on Recurrent Neural Networks (RNNs)
Show steps
Practice implementing and training RNNs to improve understanding of the concepts discussed in Week 3.
Browse courses on
Recurrent Neural Networks
Show steps
-
Find online platforms or textbooks that offer practice problems on RNNs.
-
Select problems that cover different aspects of RNNs, such as sequence generation or classification.
-
Implement the RNN models using a programming language like Python or R.
-
Train and evaluate the models on provided datasets.
-
Analyze the results and identify areas for improvement.
Review fundamentals of Machine Learning
Show steps
Revisit the foundational concepts of machine learning to strengthen the understanding of deep learning methodologies.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on machine learning concepts.
- Complete practice exercises or online quizzes to reinforce understanding.
Organize course notes and materials
Show steps
Consolidate and organize course materials to enhance understanding and facilitate review.
Show steps
Show steps
Show steps
- Gather all lecture notes, assignments, quizzes, and exams.
- Create a system for organizing the materials, such as folders or a digital notebook.
- Review and annotate the materials to improve comprehension.
Learn about Deep Learning applications in healthcare
Show steps
Review materials on the practical applications of deep learning in healthcare to enhance understanding of the course content.
Show steps
Show steps
Show steps
- Explore online resources, such as research papers or industry articles, that showcase deep learning applications in healthcare.
- Identify specific examples of how deep learning is used to solve healthcare problems, such as disease diagnosis or drug discovery.
- Analyze the benefits and challenges of using deep learning in healthcare.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a glossary of Deep Learning terms
Show steps
Compile a comprehensive glossary of key terms and concepts to enhance understanding and retention of the course content.
Show steps
Show steps
Show steps
- Identify important terms and concepts from the course materials.
- Define each term clearly and concisely.
- Organize the terms alphabetically or by category.
Complete coding exercises on Healthcare Data
Show steps
Practice working with healthcare data to enhance understanding of the data handling techniques discussed in the course.
Browse courses on
Healthcare Data
Show steps
Show steps
Show steps
- Find online platforms or tutorials that provide coding exercises on healthcare data.
- Select exercises that involve data preprocessing, feature engineering, or model evaluation.
- Implement the code using a programming language like Python or R.
- Analyze the results and identify areas for improvement.
Consolidate knowledge of Convolutional Neural Networks (CNNs)
Show steps
Review materials on CNNs to reinforce understanding of the concepts discussed in Week 2.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Identify reliable online resources for CNN tutorials.
- Follow video tutorials or read articles explaining the concepts of CNNs.
- Work through the provided examples or exercises to practice implementing CNNs.
Engage in peer discussions on Autoencoders
Show steps
Collaborate with peers to explore and understand the concepts of Autoencoders covered in Week 4.
Browse courses on
Autoencoders
Show steps
Show steps
Show steps
- Form study groups or join online forums dedicated to deep learning.
- Choose a topic related to Autoencoders for discussion.
- Prepare by reading research papers or reviewing course materials on the topic.
- Engage in discussions, share insights, and ask questions to enhance understanding.
Solve practice problems on Recurrent Neural Networks (RNNs)
Show steps
Practice implementing and training RNNs to improve understanding of the concepts discussed in Week 3.
Browse courses on
Recurrent Neural Networks
Show steps
Show steps
Show steps
- Find online platforms or textbooks that offer practice problems on RNNs.
- Select problems that cover different aspects of RNNs, such as sequence generation or classification.
- Implement the RNN models using a programming language like Python or R.
- Train and evaluate the models on provided datasets.
- Analyze the results and identify areas for improvement.
Career center
Learners who complete Deep Learning Methods for Healthcare will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Deep Learning Methods for Healthcare is an excellent course for aspiring Data Scientists. With healthcare applications at the forefront of the field, this course provides foundational knowledge and training that can accelerate your transition into a Data Science career. You will learn foundational deep learning concepts like embedding, convolutional neural networks, recurrent neural networks, and autoencoders. The hands-on programming labs will provide practical experience in developing deep learning models for healthcare data, a crucial skill for Data Scientists in this domain. The course includes a large project that encourages you to apply your learnings to address specific healthcare problems, further enhancing your portfolio and demonstrating your expertise in healthcare data analysis.
Machine Learning Engineer
Machine Learning Engineer
Deep Learning Methods for Healthcare is highly recommended for those seeking a career as a Machine Learning Engineer specializing in healthcare. The course provides comprehensive coverage of deep learning methods and their applications in healthcare, equipping you with the necessary skills to develop and deploy machine learning solutions for healthcare challenges. Through video lectures, self-guided labs, and homework assignments, you will gain a strong understanding of embedding, convolutional neural networks, recurrent neural networks, and autoencoders. The course's focus on healthcare applications will give you a competitive edge in this rapidly growing field.
Healthcare Data Analyst
Healthcare Data Analyst
Deep Learning Methods for Healthcare is an exceptional choice for individuals pursuing a career as a Healthcare Data Analyst. The course provides a deep dive into deep learning techniques and their applications in healthcare, empowering you to analyze and interpret complex healthcare data effectively. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to extract meaningful insights from healthcare data. The course's emphasis on practical applications will equip you with the skills to contribute to data-driven decision-making in healthcare organizations.
Research Scientist
Research Scientist
For those interested in a career as a Research Scientist in healthcare, Deep Learning Methods for Healthcare offers valuable theoretical and practical knowledge. The course provides a comprehensive overview of deep learning methods and their applications in healthcare research. You will gain expertise in embedding, convolutional neural networks, recurrent neural networks, and autoencoders, which are essential techniques for analyzing and interpreting large healthcare datasets. The course's focus on healthcare applications will give you a strong foundation for conducting impactful research in this field.
Software Engineer
Software Engineer
Deep Learning Methods for Healthcare can be beneficial for Software Engineers seeking to specialize in healthcare software development. The course provides a comprehensive introduction to deep learning methods and their applications in healthcare, enabling you to develop innovative software solutions for healthcare challenges. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to create software that can analyze and interpret healthcare data, leading to improved patient outcomes and healthcare delivery.
Healthcare Consultant
Healthcare Consultant
For individuals pursuing a career as a Healthcare Consultant, Deep Learning Methods for Healthcare offers valuable insights and practical knowledge. The course provides a deep understanding of deep learning methods and their applications in healthcare, equipping you to advise healthcare organizations on implementing data-driven solutions. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze healthcare data, identify trends, and make recommendations for improving healthcare delivery and patient outcomes.
Product Manager
Product Manager
Deep Learning Methods for Healthcare can be beneficial for Product Managers in the healthcare industry. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to develop innovative healthcare products and services. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to create products that can analyze and interpret healthcare data, leading to improved patient outcomes and healthcare delivery.
Data Engineer
Data Engineer
Deep Learning Methods for Healthcare may be useful for Data Engineers seeking to specialize in healthcare data management. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to design and manage data architectures that can support deep learning models. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to optimize data storage, processing, and retrieval for healthcare applications.
Business Analyst
Business Analyst
Deep Learning Methods for Healthcare may be useful for Business Analysts in the healthcare industry. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to analyze business needs and develop data-driven solutions. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze healthcare data, identify trends, and make recommendations for improving business processes and patient outcomes.
Statistician
Statistician
Deep Learning Methods for Healthcare may be useful for Statisticians seeking to specialize in healthcare data analysis. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to develop and apply statistical models for healthcare research and decision-making. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze healthcare data, identify trends, and make predictions for improving patient outcomes and healthcare delivery.
Epidemiologist
Epidemiologist
Deep Learning Methods for Healthcare may be useful for Epidemiologists seeking to leverage deep learning for disease surveillance and outbreak prediction. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to develop and apply deep learning models for analyzing healthcare data and identifying patterns and trends. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze healthcare data, identify risk factors, and make predictions for improving public health outcomes.
Clinical Research Associate
Clinical Research Associate
Deep Learning Methods for Healthcare may be useful for Clinical Research Associates seeking to enhance their data analysis skills. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to analyze clinical data and develop data-driven insights. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze clinical data, identify trends, and make recommendations for improving clinical trial design and patient outcomes.
Health Informatics Specialist
Health Informatics Specialist
Deep Learning Methods for Healthcare may be useful for Health Informatics Specialists seeking to specialize in deep learning applications in healthcare. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to design and implement deep learning solutions for healthcare data management and analysis. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze healthcare data, develop predictive models, and improve healthcare delivery.
Medical Writer
Medical Writer
Deep Learning Methods for Healthcare may be useful for Medical Writers seeking to enhance their understanding of deep learning in healthcare. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to effectively communicate complex technical concepts to a non-technical audience. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze healthcare data, identify trends, and develop clear and concise written materials for healthcare professionals and patients.
Healthcare Administrator
Healthcare Administrator
Deep Learning Methods for Healthcare may be useful for Healthcare Administrators seeking to understand the potential of deep learning in healthcare management. The course provides a comprehensive overview of deep learning methods and their applications in healthcare, enabling you to make informed decisions about adopting deep learning solutions for healthcare data analysis and decision-making. You will learn how to leverage embedding, convolutional neural networks, recurrent neural networks, and autoencoders to analyze healthcare data, identify trends, and develop data-driven strategies for improving healthcare operations and patient outcomes.
For more career information including salaries, visit:
OpenCourser.com/course/mx3fma/deep
Reading list
We've selected five 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
Deep Learning Methods for Healthcare.
Specializes in deep learning for medical image analysis, covering topics such as image segmentation, registration, and classification in the context of medical imaging.
Comprehensive guide to deep learning using Python. It covers various deep learning architectures and techniques. It valuable resource for gaining a strong foundation in deep learning.
Comprehensive guide to data analysis using Python. It covers various topics such as data cleaning, manipulation, and visualization. It valuable resource for gaining a strong foundation in data analysis using Python.
Provides a comprehensive introduction to machine learning using Python. It covers various machine learning algorithms and techniques. It valuable resource for gaining a solid foundation in machine learning.
Provides a comprehensive introduction to natural language processing using Python. It covers various NLP techniques and applications. It valuable resource for gaining a solid foundation in NLP.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mx3fma/deep
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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
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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 (viewing)
- Advanced Deep Learning Methods for Healthcare
Good to know
Teaches deep learning methods, which are crucial for healthcare applications
Provides hands-on experience through programming labs and homework assignments
Capstone project allows learners to apply their knowledge and skills to address real-world healthcare problems
Covers various deep learning techniques relevant to healthcare, including embedding, CNNs, RNNs, and autoencoders
Led by instructors with expertise in healthcare data and applications
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