Announcing the first-ever course on Data-Centric AI. Learn how to train better ML models by improving the data.

Course homepage | Lecture videos on YouTube | Lab Assignments

The course covers:

• Data-Centric AI vs. Model-Centric AI
• Label Errors
• Dataset Creation and Curation
• Data-centric Evaluation of ML Models
• Class Imbalance, Outliers, and Distribution Shift
• Growing or Compressing Datasets
• Interpretability in Data-Centric ML
• Encoding Human Priors: Data Augmentation and Prompt Engineering
• Data Privacy and Security

MIT, like most universities, has many courses on machine learning (6.036, 6.867, and many others). Those classes teach techniques to produce effective models for a given dataset, and the classes focus heavily on the mathematical details of models rather than practical applications. However, in real-world applications of ML, the dataset is not fixed, and focusing on improving the data often gives better results than improving the model. We’ve personally seen this time and time again in our applied ML work as well as our research.

Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets in a systematic/algorithmic way — given that this topic wasn’t covered in the standard curriculum, we (a group of PhD candidates and grads) thought that we should put together a new class! We taught this intensive 2-week course in January over MIT’s IAP term, and we’ve just published all the course material, including lecture videos, lecture notes, hands-on lab assignments, and lab solutions, in hopes that people outside the MIT community would find these resources useful.

We’d be happy to answer any questions related to the class or DCAI in general, and we’d love to hear any feedback on how we can improve the course material. Introduction to Data-Centric AI is open-source opencourseware, so feel free to make improvements directly: https://github.com/dcai-course/dcai-course.