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Interpretable Machine Learning

UVa CS 4501/6501 (Spring 2022)

1. Course Information

2. Course Description

Machine learning models have achieved remarkable performance in a wide range of AI fields, such as Natural Language Processing and Computer Vision. However, the lack of interpretability of machine learning models raises concerns regarding the trustworthiness and reliability of their predictions. This problem blocks their applications in the real world, especially in high-stake scenarios, such as healthcare, economy and criminal justice. The goal of this course is to let students get familiar with the emerging problem in machine learning and recent advances in interpretable and explainable AI.

2.1 Topics

This course will include but not limit to the following contents:

2.2 Format

2.3 Prerequisites

2.4 Textbook/Materials

3. Assignments and Evaluation Schemes

4. Additional Information


Hanjie Chen is supported by the UVa Engineering Graduate Teaching Internship Program (GTI) for designing and teaching this course.