CS 525 surveys the landscape of data for AI with a focus on contemporary learning problems such as training language models or multimodal models. Students will learn about important datasets and common data processing methods including filtering and deduplication. Further topics will include synthetic data, data attribution, and environments for reinforcement learning. The course will also cover ethical and legal aspects of training data such as copyright and privacy. Over the course of the class, students will build a training set for a learning problem of their choice. The class will consist of faculty lectures, student presentations, and guest lectures.
3 units · Letter or Credit/No Credit
CS525 surveys the landscape of data for AI with a focus on contemporary learning problems such as training language models or multimodal models. Students will learn about important datasets and common data processing methods including filtering and deduplication. Further topics will include synthetic data, data attribution, and environments for reinforcement learning. The course will also cover ethical and legal aspects of training data such as copyright and privacy. Over the course of the class, students will build a training set for a learning problem of their choice. The class will consist of faculty lectures, student presentations, and guest lectures.
Offered in Winter 2027 at Stanford University.