Residing between supervised and unsupervised learning, semi-supervised learning accepts data that’s partially labeled or where the majority of the data lacks labels.

Describe text classification and related terminology (e.g., supervised machine learning). Apply text classification to marketing data through a peer-graded project. Apply text classification to a ...

U. Jo and S. B. Kim, “Semi-Supervised Learning with Wafer-Specific Augmentations for Wafer Defect Classification,” in IEEE Access, doi: 10.1109/ACCESS.2024.3522180. Tags: data augmentation defect ...

This week we will learn about non-parametric models. k-Nearest Neighbors makes sense on an intuitive level. Decision trees are a supervised learning model that can be used for either regression or ...

For classification tasks, the lack of enough labeled images in the training set often results in overfitting. Another issue is the mismatch between the training and the test domains, which results in ...

The AI model can learn from data that’s already out there, without any special labels. Self-supervised learning enables pre-training an AI model on massive amounts of general-purpose data. That way, ...

This week, I debated with my friend whether one should consider that Generative AI tools are created through supervised or unsupervised learning. At the end of it, I lost the debate.