dcai-lab and dcai-course
These are **complements** — the lab repository contains hands-on coding assignments that accompany and reinforce the concepts taught in the course repository.
About dcai-lab
dcai-course/dcai-lab
Lab assignments for Introduction to Data-Centric AI, MIT IAP 2024 👩🏽💻
Covers nine progressive labs spanning label error detection via Confident Learning, multi-annotator dataset curation, outlier identification, active learning, feature interpretability, and membership inference attacks. Jupyter notebooks combine hands-on implementation of data-centric techniques (data quality improvement, augmentation, prompt engineering) with black-box model evaluation, emphasizing how dataset engineering outperforms model-centric optimization. Integrates with standard ML libraries and LLMs, with Labs 8+ supporting Colab execution for zero-setup experimentation.
About dcai-course
dcai-course/dcai-course
Introduction to Data-Centric AI, MIT IAP 2024 🤖
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