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

4,508 already enrolled

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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 videosTotal 30 minutes

Welcome to this course!3 minutes
Attention Models: Introduction13 minutes
Applied Attention Models: Retain8 minutes
Applied Attention Models: Heart Failure Example6 minutes

3 readingsTotal 80 minutes

About this course10 minutes
Slides: Attention Models10 minutes
Textbook Chapter 9 (Attention)60 minutes

1 assignmentTotal 30 minutes

Attention Model30 minutes

1 programming assignmentTotal 180 minutes

Homework 4 (RETAIN)180 minutes

1 discussion promptTotal 10 minutes

Welcome Forum10 minutes

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

What's included

5 videos2 readings1 assignment1 programming assignment

5 videosTotal 36 minutes

GNN: Key Components12 minutes
GNN: General Design7 minutes
GCN8 minutes
MPNN5 minutes
GAT4 minutes

2 readingsTotal 70 minutes

Slides: GNN10 minutes
Textbook Chapter 10 (Graph Neural Networks)60 minutes

1 assignmentTotal 30 minutes

Graph Neural Networks30 minutes

1 programming assignmentTotal 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 videosTotal 87 minutes

Memory Networks12 minutes
End-to-end Memory Networks7 minutes
Transformer: Introduction5 minutes
Self Attention: Part 110 minutes
Self Attention: Part 212 minutes
Doctor2Vec18 minutes
GAMENet13 minutes
G-BERT11 minutes

2 readingsTotal 70 minutes

Slides: Memory Networks10 minutes
Textbook Chapter 11 (Memory Networks)60 minutes

1 assignmentTotal 30 minutes

Memory Network30 minutes

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

What's included

4 videos2 readings1 assignment

4 videosTotal 55 minutes

Generative Adversarial Network (GAN) - Method12 minutes
Generative Adversarial Network (GAN) - Application16 minutes
Variational Autoencoder (VAE) - Method17 minutes
Variational Autoencoder (VAE) - Application11 minutes

2 readingsTotal 70 minutes

Slides: Generative Models10 minutes
Textbook Chapter 12 (Generative Models)60 minutes

1 assignmentTotal 30 minutes

Generative Models30 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 Courses12,032 learners

Offered by

University of Illinois Urbana-Champaign

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