Interpretable Machine Learning
Master interpretable ML techniques in Python, from regression and decision trees to neural networks and mechanistic interpretability.
Master interpretable ML techniques in Python, from regression and decision trees to neural networks and mechanistic interpretability.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Explainable AI (XAI) Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Describe and implement regression and generalized models
Explain and implement decision trees and rules
Demonstrate knowledge of interpretable neural networks
Understand mechanistic interpretability concepts
Analyze representation learning in LLMs
Skills you'll gain
This course includes:
1.78 Hours PreRecorded video
3 assignments, 7 labs
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This comprehensive programming course focuses on implementing interpretable machine learning techniques. The curriculum covers regression models, decision trees, neural networks, and mechanistic interpretability concepts. Through hands-on Python programming labs, students learn to create transparent, ethical AI systems for critical domains.
Regression and Generalized Models
Module 1 · 5 Hours to complete
Rules, Trees, and Neural Networks
Module 2 · 4 Hours to complete
Introduction to Mechanistic Interpretability
Module 3 · 3 Hours to complete
Fee Structure
Instructor
Pioneering Responsible AI and Machine Learning Innovator at Duke University
Dr. Brinnae Bent is an esteemed faculty member in Artificial Intelligence at Duke University, where she serves as the Executive in Residence for the Master of Engineering in Artificial Intelligence program. With a robust background that bridges research and industry, Dr. Bent has led significant projects and developed impactful algorithms for major global companies, focusing on applications that enhance human health and well-being, such as noninvasive glucose monitoring and assistive technologies for mobility. She is a prolific researcher with over 30 publications, recognized for her groundbreaking work on digital biomarkers and her commitment to advancing Responsible AI practices. As an educator, Dr. Bent teaches core courses in the AI program and introduces innovative electives, including a forthcoming course on Explainable AI. Outside of her academic pursuits, she curates a weekly tech newsletter called “Spill the GPTea” and balances her professional life with personal interests as a mother, ultramarathoner, and artist. Dr. Bent's contributions to AI education and research position her as a leading voice in the field, dedicated to solving real-world challenges through technology.
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