Practical Methodology and Ethics in AI

Overview

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.

Syllabus

• Course Introduction
"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.
• Practical Methodology
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.
• Ethical Considerations
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.
• Structured Probabilistic Models
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.

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Taught by

Zerotti Woods

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Subjects

Data Science