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Practical Methodology and Ethics in AI
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Johns Hopkins University

Practical Methodology and Ethics in AI

This course is part of Foundations of Neural Networks Specialization

Zerotti Woods

Instructor: Zerotti Woods

Included with Coursera Plus

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4 modules
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

Recommended experience

Intermediate level

A basic understanding of machine learning, deep learning, probability and familiarity with Python and neural networks is recommended.

6 hours to complete
Flexible schedule
Learn at your own pace

4 modules
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

Recommended experience

Intermediate level

A basic understanding of machine learning, deep learning, probability and familiarity with Python and neural networks is recommended.

6 hours to complete
Flexible schedule
Learn at your own pace
  • About
  • Outcomes
  • Modules
  • Recommendations
  • Testimonials

What you'll learn

  • Learners will gain hands-on experience training and debugging deep learning models while considering deployment challenges and best practices.

  • Students will understand and evaluate ethical concerns in AI, including bias, fairness, and the societal impact of deploying neural networks.

  • Learners will explore how to integrate structured probabilistic models with deep learning, reducing uncertainty and improving model decision-making.

Skills you'll gain

  • Deep Learning
  • Data Ethics
  • Social Studies
  • Ethical Standards And Conduct
  • Machine Learning
  • Unstructured Data
  • Information Privacy
  • Data-Driven Decision-Making
  • Applied Machine Learning
  • Probability Distribution
  • Artificial Intelligence
  • Debugging
  • Artificial Intelligence and Machine Learning (AI/ML)

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

6 assignments

Taught in English

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This course is part of the Foundations of Neural Networks Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

The course "Practical Methodologies and Ethics in AI" equips learners with the essential skills needed to build, evaluate, and deploy deep learning models, while also addressing critical ethical considerations in AI. Through hands-on projects and case studies, you’ll explore the practical methodologies used to train models effectively, troubleshoot issues, and apply structured probabilistic approaches to manage uncertainty. A key highlight of the course is its emphasis on ethics, enabling you to identify and address bias, fairness, and societal implications throughout the AI lifecycle. By integrating structured probabilistic models with deep learning, you’ll gain the ability to create robust, interpretable AI systems that tackle real-world challenges.

What sets this course apart is its balanced focus on technical mastery and responsible AI practices. You’ll learn to handle incomplete data, analyze peer presentations, and critically evaluate AI’s broader societal impact. Whether you’re a data scientist or an AI enthusiast, this course will provide a comprehensive foundation to develop impactful and ethical AI solutions.

"Practical Methodology and Ethics in AI" focuses on teaching essential skills in dataset exploration, training deep learning models, and deploying them, with a strong emphasis on ethics in the AI lifecycle. The course covers identifying and addressing bias and fairness issues and integrating probabilistic models with deep learning to manage uncertainty. This course provides a solid foundation in both technical and ethical aspects for responsible AI development.

What's included

2 readings

2 readings•Total 10 minutes
  • Course Overview•5 minutes
  • Instructor Biography: Prof. Zerotti Woods•5 minutes

This module will discuss practical methodologies for training Deep Learning Models. Students will explore case studies along with different situations to apply previous and new knowledge in the process of training and deploying Deep Learning Models.

What's included

1 video1 reading2 assignments

1 video•Total 7 minutes
  • Practical Methodology•7 minutes•Preview module
1 reading•Total 45 minutes
  • Reading References•45 minutes
2 assignments•Total 75 minutes
  • Practical Methodology •60 minutes
  • Practical Methodology•15 minutes

This module will discuss ethical considerations for Deep Learning Models. You will explore nuances of ethics and the use of machine learning to make decisions.

What's included

1 video1 reading2 assignments

1 video•Total 21 minutes
  • Ethics in AI•21 minutes•Preview module
1 reading•Total 10 minutes
  • Reading References•10 minutes
2 assignments•Total 75 minutes
  • Ethical Considerations•60 minutes
  • Ethics in AI•15 minutes

This lesson delves into the intersection of structured probabilistic models and deep neural networks, highlighting how probabilistic frameworks can be integrated with Deep Learning to model uncertainty, learn from incomplete data, and provide interpretable AI systems.

What's included

1 video1 reading2 assignments

1 video•Total 26 minutes
  • Structured Probabilistic Modeling for Deep Learning•26 minutes•Preview module
1 reading•Total 70 minutes
  • Reading References•70 minutes
2 assignments•Total 75 minutes
  • Structured Probabilistic Models•60 minutes
  • Structural Probabilistic Models•15 minutes

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Instructor

Zerotti Woods
Zerotti Woods
Johns Hopkins University
3 Courses•989 learners

Offered by

Johns Hopkins University

Offered by

Johns Hopkins University

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

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