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Introduction to Interpretable Machine Learning - Cynthia Rudin

Offered By: Institute for Advanced Study via YouTube

Tags

Interpretable Machine Learning Courses Machine Learning Courses Information Theory Courses Vectors Courses Classification Courses Decision Trees Courses

Course Description

Overview

Explore the fundamentals of interpretable machine learning in this 57-minute lecture from the 2022 Program for Women and Mathematics. Delve into key concepts such as vectors, classification, and natural language processing as Duke University's Cynthia Rudin presents the Terng Lecture. Gain insights into model complexity, decision trees, and information theory while examining practical demonstrations and real-world data sets. Discover how these principles contribute to creating transparent and understandable machine learning models, essential for various applications in today's data-driven world.

Syllabus

Introduction
Machine Learning
Vectors
Classification
Demonstration
Natural Language Processing
Model Complexity
Decision Trees
Data Set
Information Theory


Taught by

Institute for Advanced Study

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