Introduction to Data-Centric AI
A new course on Data-Centric AI (DCAI) will be offered from January 16 to January 26, 2024. This course focuses on improving datasets to enhance machine learning performance, rather than solely refining models. It includes practical techniques and hands-on programming assignments to address common data issues in supervised learning tasks.
- ▪The course is designed to teach systematic techniques for dataset improvement in machine learning applications.
- ▪Participants will engage in lab assignments using Python and Jupyter Notebook, either individually or in groups.
- ▪No prior credit is required to enroll, but a basic understanding of machine learning and Python is recommended.
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Introduction to Data-Centric AI IAP 2024 Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While good data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline. This is the first-ever course on DCAI.
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