As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course is a comprehensive, hands-on guide to Interpretable Machine Learning, empowering you to develop AI solutions that are aligned with responsible AI principles. You will also gain an understanding of the emerging field of Mechanistic Interpretability and its use in understanding large language models.
Recommended experience
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
Skills you'll gain
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September 2024
3 assignments
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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
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