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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/health-data-science-foundation
课程评论：没有评论
课程名称：健康数据科学基础 课程概述：本课程面向对机器学习感兴趣的医疗专业人士，或对医疗应用有兴趣的计算机科学专业人士。课程内容将涵盖健康数据分析、各种神经网络类型，以及在实际医疗场景中应用神经网络的训练与应用。我们将深入探讨深度学习（DL）方法、医疗数据及其在DL方法中的应用。课程包括视频讲座、自主编程实验、书面和编程作业，以及一个大型项目。 课程的第一阶段将包括关于各种深度学习和健康应用主题的视频讲座、自主实验室与多个作业。在这一阶段，学员将积累有关在医疗数据上开发实际深度学习模型的知识和经验。课程的第二阶段将是一个大型项目，可能会产生技术报告和深度学习模型的功能展示，以解决一些特定的医疗问题。我们期待优秀的项目有潜力发表科学论文。 课程大纲： 第一部分：第一周 - 介绍 描述：在介绍环节中，我们将介绍课程主题及相关背景信息。 第二部分：第二周 - 健康数据 描述：健康数据来源于多种医疗服务类别。我们将对此进行深入分析，并探讨这对健康数据标准的影响。 第三部分：第三周 - 机器学习基础 描述：本周主题为机器学习。我们将学习机器学习的基本概念。 第四部分：第四周 - 深度神经网络（DNN） 描述：将进一步探讨深度神经网络的原理与应用。
Part: 1
Title:Week 1 - Introduction
Description:In the introduction we will introduce the topic of the course and present the background information.
Part: 2
Title:Week 2 - Health Data
Description:Health Data are generated in many different categories of medical services. We'll take a closer look at these, and what this means for Health Data standards.
Part: 3
Title:Week 3 - Machine Learning Basics
Description:The topic of this week is Machine Learning. We'll look at
Part: 4
Title:Week 4 - Deep Neural Networks (DNN)
Description:
This course is intended for persons involved in machine learning who are interested in medical applications, or vice versa, medical professionals who are interested in the methods modern computer science has to offer to their field. We will cover health data analysis, different types of neural networks, as well as training and application of neural networks applied on real-world medical scenarios. We cover 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.
