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M06CO3CO4
Foundation Models, Deployment and Responsible Vision
Self-supervised learning with InfoNCE, the attention mechanism, and CLIP and SAM as multimodal foundation models; then deployment concerns of distribution shift, fairness, latency, monitoring, and structured failure analysis, closing with course synthesis and project presentations. The final examination follows this module.
Explain InfoNCE, attention, and how CLIP and SAM are trained and used
Diagnose distribution shift and design robustness and fairness evaluations
Plan latency, monitoring, and failure-analysis practice for deployed systems
Synthesize the course arc from image signals to foundation models
PyTorchHugging Face TransformersONNX RuntimeOpenCV
Lectures in this Module(2)
L24
SSL, Transformers, and Foundation Models
InfoNCE contrastive learning, the attention mechanism, and CLIP and SAM as multimodal vision foundations.
CO3
L25Deployment, Robustness, Ethics, and Review
Distribution shift, fairness, latency budgets, monitoring, and structured failure analysis, plus course synthesis.
CO3CO4