In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.
Este curso faz parte do Programa de cursos integrados Ethics in the Age of AI
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Curso 2 de 4 no
Nível iniciante
Aprox. 6 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
Habilidades que você terá
- machine learning fairness
- Ethics
- data bias
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 4 no
Nível iniciante
Aprox. 6 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido por
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
Programa - O que você aprenderá com este curso
2 horas para concluir
Fairness and protections in machine learning
Welcome to the course! In week one, we will be discussing what fairness means in the context of machine learning and what true parity means in different scenarios
2 horas para concluir
5 vídeos (Total 19 mín.), 2 leituras, 3 testes
2 horas para concluir
Building fair models: theory and practice
This week we will take action against unfairness. Now that we have an understanding of fairness issues, how do we build models that do not violate them?
2 horas para concluir
5 vídeos (Total 21 mín.), 2 leituras, 3 testes
2 horas para concluir
Human factors: minimizing bias in data
This week, we will tackle the human biases that enter the data collection and attribute selection processes. The goal? Removing bias before the model is built
2 horas para concluir
5 vídeos (Total 33 mín.), 2 leituras, 3 testes
Avaliações
Principais avaliações do ARTIFICIAL INTELLIGENCE DATA FAIRNESS AND BIAS
por AV14 de Jan de 2021
Extraodinary course! I've learnt so much! The classes are very informative and dynamic. Didn't feel like studying but rather entertaining myself with hight quality content! Thank you so much!
por CM2 de Out de 2021
An excellent reminder that the bias-variance trade-off is not the only trade-off machine learning specialists make.
por SY30 de Mar de 2021
A relatively short and interesting course on data fairness and bias impacting AI models.
Sobre Programa de cursos integrados Ethics in the Age of AI
As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations. In this specialization, we will explore the rise of algorithms, fundamental issues of fairness and bias in machine learning, and basic concepts involved in security and privacy of machine learning projects. We'll finish with a study of 3 projects that will allow you to put your new skills into action.
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