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Deep Learning Methods for Healthcare
  • Level Expert
  • Duration 22 hours
  • Course by University of Illinois Urbana-Champaign
  • Offered by

About

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.

Modules

Introduction

    • 1 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Welcome Forum

1 Videos

  • Welcome to this course!

1 Readings

  • About this course

General Embedding

    • 2 Videos
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2 Videos

  • General Embedding: Word2Vec
  • General Embedding: t-SNE

EHR Embedding

    • 2 Videos
    • 1 Readings
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2 Videos

  • EHR Embedding: Med2Vec
  • EHR Embedding: MiME

1 Readings

  • Slides: Embedding

Textbook

    • 1 Readings
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1 Readings

  • Textbook Chapter 5 (Embedding)

Quiz

    • 1 Assignment
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1 Assignment

  • Embedding

Lab

    • 1 Programming
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1 Programming

  • Lab 3

Convolution and Pooling

    • 3 Videos
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3 Videos

  • Introduction: Convolution and Pooling
  • Convolution
  • Pooling

CNN Architecture and Applications

    • 2 Videos
    • 1 Readings
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2 Videos

  • CNN Architectures
  • Healthcare Applications of CNN

1 Readings

  • Slides: CNN

Textbook

    • 1 Readings
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1 Readings

  • Textbook Chapter 6 (Convolutional Neural Networks (CNN))

Quiz

    • 1 Assignment
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1 Assignment

  • CNN

Homework

    • 1 Programming
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1 Programming

  • Homework 3 (Convolutional Neural Network)

Recurrent Neural Networks (RNN)

    • 5 Videos
    • 1 Readings
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5 Videos

  • Basic RNN Concepts & Structure
  • Back Propagation Through Time (BPTT)
  • Long Short Term Memory Networks (LSTM)
  • Gated Recurrent Unit (GRU)
  • Healthcare Applications

1 Readings

  • Slides: RNN

Textbook

    • 1 Readings
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1 Readings

  • Textbook Chapter 7 (Recurrent Neural Networks (RNN))

Quiz

    • 1 Assignment
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1 Assignment

  • RNN

Lab

    • 1 Programming
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1 Programming

  • Lab 4

Homework

    • 1 Programming
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1 Programming

  • Homework 3 (Recurrent Neural Network)

Autoencoders

    • 4 Videos
    • 1 Readings
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4 Videos

  • Autoencoders: Intro
  • Autoencoders: Properties
  • Autoencoders: Variants
  • Autoencoders: Application

1 Readings

  • Slides: Autoencoders

Textbook

    • 1 Readings
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1 Readings

  • Textbook Chapter 8 (Autoencoders (AE))

Quiz

    • 1 Assignment
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1 Assignment

  • Autoencoders

Auto Summary

Embark on an advanced journey into "Deep Learning Methods for Healthcare," a specialized course designed for data science and AI enthusiasts looking to make an impact in the healthcare sector. This expert-level course, offered by Coursera, delves into the intersection of deep learning and healthcare data, equipping learners with the skills to develop cutting-edge solutions for real-world health challenges. Guided by comprehensive video lectures and self-paced programming labs, the course unfolds in two dynamic phases. The initial phase focuses on building foundational knowledge through interactive activities, including written and programming homework assignments. Students will gain hands-on experience by creating practical deep learning models tailored to healthcare applications. The course culminates in an extensive project, where learners apply their acquired skills to address specific healthcare problems. This capstone project not only enhances technical proficiency but also offers the potential for contributions to scientific literature through technical reports and functional demos. With a total duration of 1320 minutes, the course offers flexible subscription options, including Starter and Professional plans, catering to different learning needs. Ideal for seasoned professionals and those with substantial background in data science and AI, this course is a gateway to mastering deep learning in the healthcare domain and driving innovation in medical technology.

Jimeng Sun
Instructor

Jimeng Sun