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Brent Summers
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/muwnsd/artificial
What's inside
Syllabus
Privacy and convenience vs big data
In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning
Protecting Privacy: Theories and Methods
Read more
Syllabus
Privacy and convenience vs big data
In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning
Protecting Privacy: Theories and Methods
Read more
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
Building Transparent Models
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
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for professionals working in ML seeking to center ethics and privacy in their work
Appeals to data scientists, AI engineers, executives, and students with an interest in data ethics
Suitable for beginners in the field of AI security
Provides a comprehensive overview of key concepts in the field
May require prior knowledge of machine learning algorithms and techniques
Save this course
Save Artificial Intelligence Privacy and Convenience to your list so you can find it easily later:
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Reviews summary
Practical ai privacy
According to students, Artificial Intelligence Privacy and Convenience is a superbly rated course from learnQuest that provides practical, real world steps to protect privacy while using artificial intelligence.
Learners say that the clear, simple classes are interesting and eye opening for beginners. They report that the concepts are easy to grasp and that the course is a great introduction to the complexities of algorithmic privacy. While the course is relatively short, learners say they learned a lot, even those with a background in the subject.
Classes are clear and concise.
"Classes are very clear and concise."
Concepts are easy to understand for beginners.
"The concepts were easier to grasp and a nice introduction into the complexities around algorithmic models and building ethical practices from the outset."
Covers practical ways to protect privacy with AI.
"This course provides practical steps to protect privacy."
"It tells how anonymization should be done."
Some learners wanted more depth and use cases.
"It can go little more deeper with few more use cases so that the learners can relate to real world applications of privacy."
"Some aspects of privacy and explainability are covered in this course. It did not cover enough the correlations between privacy and accuracy though"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Artificial Intelligence Privacy and Convenience with these
activities:
Re-examine security and privacy fundamentals
Show steps
Reinforce your understanding of key concepts related to security and privacy in data analysis and machine learning.
Browse courses on
Security
Show steps
-
Review course materials from previous courses or online resources on security and privacy in data science.
-
Attend a webinar or workshop on data security and privacy best practices.
Organize and synthesize course materials
Show steps
Improve your retention and understanding by organizing and synthesizing the key concepts and materials from the course.
Show steps
-
Review lecture notes, readings, and assignments to identify the most important concepts.
-
Create a summary document or mind map that connects and organizes these concepts.
-
Regularly review and update your summary to reinforce your understanding.
Review privacy terms
Show steps
Reviewing privacy policies will help you understand the legal implications of data collection and usage, preparing you for topics such as dataset security and privacy concerns in ML.
Browse courses on
Data Privacy
Show steps
-
Read privacy policies of popular websites and apps.
-
Identify the types of data being collected.
-
Understand how the data will be used.
-
Consider the potential risks and benefits of sharing your data.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Attend a workshop on privacy in machine learning
Show steps
Attending a workshop will provide you with an opportunity to learn from experts, network with others in the field, and gain hands-on experience with privacy-preserving techniques.
Browse courses on
Privacy in Machine Learning
Show steps
-
Research and identify relevant workshops.
-
Register for the workshop.
-
Attend the workshop and actively participate.
Gather resources on machine learning ethics
Show steps
Compiling resources on machine learning ethics will broaden your understanding of the ethical implications of ML and help you stay up-to-date with the latest developments in the field.
Browse courses on
Machine Learning Ethics
Show steps
-
Search for articles, books, and videos on machine learning ethics.
-
Organize the resources into a structured format.
-
Include resources that cover various perspectives and approaches.
Practice data anonymization techniques
Show steps
Enhance your proficiency in applying data anonymization techniques to protect user privacy.
Show steps
-
Complete online tutorials or exercises on data anonymization using tools like k-anonymity, l-diversity, or differential privacy.
-
Participate in Kaggle competitions or hackathons that focus on data anonymization challenges.
Practice data anonymization techniques
Show steps
Gaining hands-on experience in anonymizing data will strengthen your understanding of the techniques discussed in the course, enhancing your ability to preserve privacy while working with datasets.
Show steps
-
Choose a dataset with sensitive information.
-
Apply anonymization techniques such as k-anonymity, l-diversity, or differential privacy.
-
Evaluate the effectiveness of the anonymization techniques.
Contribute to an open-source privacy-enhancing tool
Show steps
Contributing to an open-source privacy-enhancing tool will allow you to engage with the community, learn from others, and apply your skills to a real-world project, enhancing your understanding of practical privacy techniques.
Browse courses on
Secure Coding
Show steps
-
Identify an open-source privacy-enhancing tool.
-
Review the codebase and identify areas where you can contribute.
-
Fork the repository and make your changes.
-
Submit a pull request with your changes.
Explore explainable AI techniques
Show steps
Gain hands-on experience with explainable AI techniques to enhance the transparency of your machine learning models.
Browse courses on
Explainable AI
Show steps
-
Follow online tutorials or documentation on explainable AI libraries like SHAP, LIME, or ELI5.
-
Apply explainable AI techniques to your own machine learning projects and analyze the results.
Develop a case study on ethical considerations in machine learning
Show steps
Deepen your understanding of ethical implications in machine learning by creating a detailed case study analysis.
Show steps
-
Identify a real-world scenario where ethical considerations are relevant to machine learning.
-
Research and analyze the ethical implications, biases, and potential risks associated with the scenario.
-
Develop recommendations and best practices for addressing the ethical considerations identified.
Build a privacy-preserving machine learning model
Show steps
Apply your knowledge to develop a machine learning model that incorporates privacy-preserving techniques.
Browse courses on
Machine Learning
Show steps
-
Select a suitable privacy-preserving technique, such as differential privacy or federated learning.
-
Implement the privacy-preserving technique in your machine learning model.
-
Evaluate the performance and privacy guarantees of your model.
Contribute to open-source projects on machine learning privacy
Show steps
Engage with the broader community and contribute to the advancement of machine learning privacy by participating in open-source projects.
Browse courses on
Open Source
Show steps
-
Identify open-source projects related to machine learning privacy, such as TensorFlow Privacy or PySyft.
-
Review the project documentation and codebase to understand the project's goals and architecture.
-
Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Re-examine security and privacy fundamentals
Show steps
Reinforce your understanding of key concepts related to security and privacy in data analysis and machine learning.
Browse courses on
Security
Show steps
Show steps
Show steps
- Review course materials from previous courses or online resources on security and privacy in data science.
- Attend a webinar or workshop on data security and privacy best practices.
Organize and synthesize course materials
Show steps
Improve your retention and understanding by organizing and synthesizing the key concepts and materials from the course.
Show steps
Show steps
Show steps
- Review lecture notes, readings, and assignments to identify the most important concepts.
- Create a summary document or mind map that connects and organizes these concepts.
- Regularly review and update your summary to reinforce your understanding.
Review privacy terms
Show steps
Reviewing privacy policies will help you understand the legal implications of data collection and usage, preparing you for topics such as dataset security and privacy concerns in ML.
Browse courses on
Data Privacy
Show steps
Show steps
Show steps
- Read privacy policies of popular websites and apps.
- Identify the types of data being collected.
- Understand how the data will be used.
- Consider the potential risks and benefits of sharing your data.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Attend a workshop on privacy in machine learning
Show steps
Attending a workshop will provide you with an opportunity to learn from experts, network with others in the field, and gain hands-on experience with privacy-preserving techniques.
Browse courses on
Privacy in Machine Learning
Show steps
Show steps
Show steps
- Research and identify relevant workshops.
- Register for the workshop.
- Attend the workshop and actively participate.
Gather resources on machine learning ethics
Show steps
Compiling resources on machine learning ethics will broaden your understanding of the ethical implications of ML and help you stay up-to-date with the latest developments in the field.
Browse courses on
Machine Learning Ethics
Show steps
Show steps
Show steps
- Search for articles, books, and videos on machine learning ethics.
- Organize the resources into a structured format.
- Include resources that cover various perspectives and approaches.
Practice data anonymization techniques
Show steps
Enhance your proficiency in applying data anonymization techniques to protect user privacy.
Show steps
Show steps
Show steps
- Complete online tutorials or exercises on data anonymization using tools like k-anonymity, l-diversity, or differential privacy.
- Participate in Kaggle competitions or hackathons that focus on data anonymization challenges.
Practice data anonymization techniques
Show steps
Gaining hands-on experience in anonymizing data will strengthen your understanding of the techniques discussed in the course, enhancing your ability to preserve privacy while working with datasets.
Show steps
Show steps
Show steps
- Choose a dataset with sensitive information.
- Apply anonymization techniques such as k-anonymity, l-diversity, or differential privacy.
- Evaluate the effectiveness of the anonymization techniques.
Contribute to an open-source privacy-enhancing tool
Show steps
Contributing to an open-source privacy-enhancing tool will allow you to engage with the community, learn from others, and apply your skills to a real-world project, enhancing your understanding of practical privacy techniques.
Browse courses on
Secure Coding
Show steps
Show steps
Show steps
- Identify an open-source privacy-enhancing tool.
- Review the codebase and identify areas where you can contribute.
- Fork the repository and make your changes.
- Submit a pull request with your changes.
Explore explainable AI techniques
Show steps
Gain hands-on experience with explainable AI techniques to enhance the transparency of your machine learning models.
Browse courses on
Explainable AI
Show steps
Show steps
Show steps
- Follow online tutorials or documentation on explainable AI libraries like SHAP, LIME, or ELI5.
- Apply explainable AI techniques to your own machine learning projects and analyze the results.
Develop a case study on ethical considerations in machine learning
Show steps
Deepen your understanding of ethical implications in machine learning by creating a detailed case study analysis.
Show steps
Show steps
Show steps
- Identify a real-world scenario where ethical considerations are relevant to machine learning.
- Research and analyze the ethical implications, biases, and potential risks associated with the scenario.
- Develop recommendations and best practices for addressing the ethical considerations identified.
Build a privacy-preserving machine learning model
Show steps
Apply your knowledge to develop a machine learning model that incorporates privacy-preserving techniques.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Select a suitable privacy-preserving technique, such as differential privacy or federated learning.
- Implement the privacy-preserving technique in your machine learning model.
- Evaluate the performance and privacy guarantees of your model.
Contribute to open-source projects on machine learning privacy
Show steps
Engage with the broader community and contribute to the advancement of machine learning privacy by participating in open-source projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source projects related to machine learning privacy, such as TensorFlow Privacy or PySyft.
- Review the project documentation and codebase to understand the project's goals and architecture.
- Identify areas where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Career center
Learners who complete Artificial Intelligence Privacy and Convenience will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Use data to solve business problems and translate data into useful insights. This course may be useful by educating you on how to protect user privacy while creating useful predictive models.
Data Scientist
Data Scientist
Develop statistical models to analyze large datasets, retrieve abstract information from patterns, and predict behaviors. This course may be helpful by educating you on how to maintain user privacy while creating useful predictive models.
Business Analyst
Business Analyst
Analyze business needs and develop solutions to improve business processes. This course may be helpful by educating you on how to protect user privacy while implementing new technologies.
Software Engineer
Software Engineer
Develop and maintain software systems. This course may be helpful by educating you on how to build secure and private software systems.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Design, develop, and maintain AI systems. This course may be useful by providing you with a foundation in AI privacy and ethics.
Privacy Lawyer
Privacy Lawyer
Advise clients on privacy laws and regulations. This course may be helpful by providing you with a foundation in AI privacy and ethics.
Compliance Analyst
Compliance Analyst
Ensure that an organization complies with laws and regulations. This course may be helpful by educating you on how to protect user privacy while implementing new technologies.
Information Security Manager
Information Security Manager
Develop and implement security policies and procedures to protect an organization's information assets. This course may be helpful by educating you on how to protect user privacy while implementing new technologies.
Privacy Consultant
Privacy Consultant
Help organizations develop and implement privacy policies and procedures. This course may be helpful by providing you with a foundation in AI privacy and ethics.
Privacy Auditor
Privacy Auditor
Audit an organization's privacy practices to ensure compliance with laws and regulations. This course may be helpful by providing you with a foundation in AI privacy and ethics.
Data Protection Consultant
Data Protection Consultant
Help organizations comply with data protection laws and regulations. This course may be helpful by providing you with a foundation in AI privacy and ethics.
Risk Analyst
Risk Analyst
Identify and assess risks to an organization. This course may be helpful by educating you on how to protect user privacy while implementing new technologies.
Security Analyst
Security Analyst
Identify and mitigate security risks. This course may be helpful by educating you on how to protect user privacy while implementing new technologies.
Privacy Analyst
Privacy Analyst
Develop and implement policies and procedures to protect user privacy. This course may be helpful by providing you with a foundation in AI privacy and ethics.
Data Protection Officer
Data Protection Officer
Ensure that an organization complies with data protection laws and regulations. This course may be helpful by providing you with a foundation in AI privacy and ethics.
For more career information including salaries, visit:
OpenCourser.com/course/muwnsd/artificial
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Artificial Intelligence Privacy and Convenience.
A comprehensive overview of the ethical dilemmas posed by AI, focusing on fairness, accountability, and trust
Save
Provides a global perspective on AI development and its implications for privacy and other societal issues
A clear and concise introduction to the ethical considerations involved in AI development and use
A detailed exploration of the legal and policy challenges posed by AI to data privacy
Provides practical guidance on implementing ML design patterns to enhance model privacy and security
This guide provides a clear overview of data privacy laws and regulations, covering topics such as data collection, use, and disclosure.
Provides a detailed overview of deep learning models for natural language processing, including text classification, text generation, and machine translation.
Explores the potential risks and challenges associated with advanced artificial intelligence systems, emphasizing the importance of aligning AI with human values.
This textbook offers a probabilistic approach to machine learning, covering topics such as Bayesian inference, graphical models, and optimization techniques.
This classic textbook provides a comprehensive overview of machine learning algorithms, including supervised and unsupervised learning, Bayesian methods, and statistical learning theory.
This textbook covers fundamental algorithms and data structures commonly used in computer science, providing a solid foundation for understanding machine learning concepts.
Save
This textbook offers an in-depth exploration of deep learning architectures and techniques, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
This practical guide focuses on applying machine learning techniques to real-world problems, using Python code and open-source libraries.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/muwnsd/artificial
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Level
Intermediate
Via
Coursera
Institution
LearnQuest
Instructor
Brent Summers
Language
English
Transcripts
Español
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中文
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This course belongs to a
collection
called
Ethics in the Age of AI. It's comprised of four courses:
- Artificial Intelligence Algorithms Models and Limitations
- Artificial Intelligence Data Fairness and Bias
- Artificial Intelligence Privacy and Convenience (viewing)
- Artificial Intelligence Ethics in Action
Good to know
Suitable for professionals working in ML seeking to center ethics and privacy in their work
Appeals to data scientists, AI engineers, executives, and students with an interest in data ethics
Suitable for beginners in the field of AI security
Provides a comprehensive overview of key concepts in the field
May require prior knowledge of machine learning algorithms and techniques
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