Duke University
Interpretable Machine Learning
Duke University

Interpretable Machine Learning

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

Recommended experience

13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Describe and implement regression and generalized interpretable models

  • Demonstrate knowledge of decision trees, rules, and interpretable neural networks

  • Explain foundational Mechanistic Interpretability concepts, hypotheses, and experiments

Details to know

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Recently updated!

September 2024

Assessments

3 assignments

Taught in English

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This course is part of the Explainable AI (XAI) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course

In this module, you will be introduced to the concepts of regression and generalized models for interpretability. You will learn how to describe interpretable machine learning and differentiate between interpretability and explainability, explain and implement regression models in Python, and demonstrate knowledge of generalized models in Python. You will apply these learnings through discussions, guided programming labs, and a quiz assessment.

What's included

5 videos6 readings1 assignment3 discussion prompts3 ungraded labs

In this module, you will be introduced to the concepts of decision trees, decision rules, and interpretability in neural networks. You will learn how to explain and implement decision trees and decision rules in Python and define and explain neural network interpretable model approaches, including prototype-based networks, monotonic networks, and Kolmogorov-Arnold networks. You will apply these learnings through discussions, guided programming labs, and a quiz assessment.

What's included

8 videos1 reading1 assignment2 discussion prompts3 ungraded labs

In this module, you will be introduced to the concept of Mechanistic Interpretability. You will learn how to explain foundational Mechanistic Interpretability concepts, including features and circuits; describe the Superposition Hypothesis; and define Representation Learning to be able to analyze current research on scaling Representation Learning to LLMs. You will apply these learnings through discussions, guided programming labs, and a quiz assessment.

What's included

6 videos4 readings1 assignment3 discussion prompts1 ungraded lab

Instructor

Brinnae Bent, PhD
Duke University
3 Courses146 learners

Offered by

Duke University

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