Advanced Deep Learning Methods for Healthcare

Advanced Deep Learning Methods for Healthcare

This course is part of Deep Learning for Healthcare Specialization

Instructor: Jimeng Sun

4,462 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

Advanced level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Prepare for a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

15 reviews

Advanced level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Prepare for a degree

Learn more

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Deep Learning for Healthcare Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

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.

What's included

4 videos3 readings1 assignment1 programming assignment1 discussion prompt

4 videos Total 30 minutes

Welcome to this course! 3 minutes
Attention Models: Introduction 13 minutes
Applied Attention Models: Retain 8 minutes
Applied Attention Models: Heart Failure Example 6 minutes

3 readings Total 80 minutes

About this course 10 minutes
Slides: Attention Models 10 minutes
Textbook Chapter 9 (Attention) 60 minutes

1 assignment Total 30 minutes

Attention Model 30 minutes

1 programming assignment Total 180 minutes

Homework 4 (RETAIN) 180 minutes

1 discussion prompt Total 10 minutes

Welcome Forum 10 minutes

In this week we'll explain the fundamentals of Graph Neural Networks.

What's included

5 videos2 readings1 assignment1 programming assignment

5 videos Total 36 minutes

GNN: Key Components 12 minutes
GNN: General Design 7 minutes
GCN 8 minutes
MPNN 5 minutes
GAT 4 minutes

2 readings Total 70 minutes

Slides: GNN 10 minutes
Textbook Chapter 10 (Graph Neural Networks) 60 minutes

1 assignment Total 30 minutes

Graph Neural Networks 30 minutes

1 programming assignment Total 180 minutes

Homework 5 (Graph Neural Network) 180 minutes

We'll explain the principles behind Memory Networks and how they can be used for predictions in medical applications.

What's included

8 videos2 readings1 assignment

8 videos Total 87 minutes

Memory Networks 12 minutes
End-to-end Memory Networks 7 minutes
Transformer: Introduction 5 minutes
Self Attention: Part 1 10 minutes
Self Attention: Part 2 12 minutes
Doctor2Vec 18 minutes
GAMENet 13 minutes
G-BERT 11 minutes

2 readings Total 70 minutes

Slides: Memory Networks 10 minutes
Textbook Chapter 11 (Memory Networks) 60 minutes

1 assignment Total 30 minutes

Memory Network 30 minutes

We'll discuss Generative Networks, as well as the method of Variational Autoencoder

What's included

4 videos2 readings1 assignment

4 videos Total 55 minutes

Generative Adversarial Network (GAN) - Method 12 minutes
Generative Adversarial Network (GAN) - Application 16 minutes
Variational Autoencoder (VAE) - Method 17 minutes
Variational Autoencoder (VAE) - Application 11 minutes

2 readings Total 70 minutes

Slides: Generative Models 10 minutes
Textbook Chapter 12 (Generative Models) 60 minutes

1 assignment Total 30 minutes

Generative Models 30 minutes

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Taking this course by University of Illinois Urbana-Champaign may provide you with a preview of the topics, materials and instructors in a related degree program which can help you decide if the topic or university is right for you.

Instructor

Instructor ratings

(7 ratings)

Jimeng Sun

University of Illinois Urbana-Champaign

4 Courses 11,871 learners

Offered by

University of Illinois Urbana-Champaign

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Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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