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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/ai-privacy-and-convenience
课程评论：没有评论
课程名称：人工智能隐私与便利性 概述：本课程将探讨机器学习项目中与安全性和隐私相关的基本概念。我们将深入研究这些决策背后的伦理问题，探索在创建有用的预测模型的同时如何保护用户免受隐私侵犯。此外，我们还将提出关于企业如何实施算法以及这对用户隐私和透明度的影响等重大问题，这些影响在现在和未来将会如何演变。 课程大纲： 1. 模块名称：隐私与便利性 vs 大数据 描述：在模块1中，我们将讨论在机器学习中真正的匿名性和隐私的含义。 2. 模块名称：保护隐私：理论与方法 描述：在模块2中，我们将深入研究数据集的安全性，并探讨在现有和新数据集中增加隐私保护的方法，以保护其中的个人。 3. 模块名称：构建透明模型 描述：在模块3中，我们将讨论如何将伦理和隐私模型付诸实践。我们会探索可解释的人工智能运动，以及为设计这些算法的团队所面临的权衡。
Name:Privacy and convenience vs big data
Description:In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning
Name:Protecting Privacy: Theories and Methods
Description:In Module 2, we are going to take a deeper look at dataset security. We will also look into methods to add privacy to existing and new datasets to protect those individuals in them
Name:Building Transparent Models
Description:In Module 3, we will discuss putting ethical, private models into practice. We will explore the explainable AI movement as well as tradeoffs for the teams putting together these algorithms
In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. Diving into the ethics behind these decisions, we will explore how to protect users from privacy violations while creating useful predictive models. We will also ask big questions about how businesses implement algorithms and how that affects user privacy and transparency now and in the future.
