Machine Learning Transparency for Practitioners focuses on equipping professionals with the essential tools and knowledge to make machine learning models interpretable and understandable. This course emphasizes practical applications, allowing participants to engage in hands-on projects that foster a deep understanding of explainable AI techniques. By the end of the program, participants will be able to implement various methods for enhancing model transparency, thereby improving communication with stakeholders and facilitating better decision-making processes.
The curriculum is designed to provide a comprehensive foundation in the principles of explainable AI, covering both theoretical concepts and practical implementations. Participants will work on real-world projects, culminating in a final project that showcases their ability to apply transparency techniques in machine learning models. This course not only prepares individuals for immediate application in their careers but also encourages the dissemination of their findings through publication in Cademix Magazine, fostering a culture of knowledge sharing within the community.
Introduction to Machine Learning Transparency
Overview of Explainable AI Techniques
Model Interpretability vs. Model Accuracy
Feature Importance and Attribution Methods
Local vs. Global Interpretability
Visualization Techniques for Model Outputs
Case Studies: Successful Implementations of XAI
Tools and Frameworks for Explainability (e.g., SHAP, LIME)
Best Practices for Communicating Results to Non-Technical Stakeholders
Final Project: Developing an Explainable Model for a Real-World Application