Mth 366 Interpretable Machine Learning Part 1

Mth 366 Interpretable Machine Learning Part 1 Information Guide

1. About to Mth 366 Interpretable Machine Learning Part 1
2. Core Information
3. Recent Updates
4. Expert Insights
5. Final Thoughts

About to Mth 366 Interpretable Machine Learning Part 1

Organizers: Bolei Zhou Laurens van der Maaten Been Kim Andrea Vedaldi Description: Complex In the first segment of the workshop, Professor Hima Lakkaraju motivates the need for SymbolicRegression.jl is a state-of-the-art symbolic regression library written from scratch in Julia using a custom evolutionary ... Remember that we did not quite manage to cover the whole This is a talk for the paper with the same name: If you want to learn more about specific methods ...

Core Information

Recent Updates

Interpretable Machine Learning Models Simply Explained - Rulefit, GA2M, Rule Lists, and Scorecard

Serg Masis - Interpretable Machine Learning with Python

Interpretable Machine Learning & Causal Inference Workshop

Interpretable Machine Learning

IML - 01 Introduction - 03 Dimensions of Interpretability

Stanford Seminar - ML Explainability Part 1 I Overview and Motivation for Explainability

Interpretable Machine Learning with SymbolicRegression.jl | Miles Cranmer | JuliaCon 2023

Interpretable Machine Learning

BADS Video Lecture 11 - Interpretable Machine Learning Part I

Alexander Engelhardt: Interpretable Machine Learning: How to make black box... | PyData Berlin 2019

Interpretable vs Explainable Machine Learning

Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges

Expert Insights

Data is compiled from public records and verified media reports.

Last Updated: May 21, 2026

Final Thoughts

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