This course goes beyond traditional keyword search to teach state-of-the-art techniques in semantic retrieval and visual grounding for AI applications. Students start by understanding semantic search fundamentals, including vector embeddings, cosine similarity, and how modern NLP models like transformers represent meaning beyond simple keywords.

The course then introduces multimodal AI concepts, combining text and visual information to enable AI systems to understand and link language with images. Learners explore visual grounding, where models can detect and align textual descriptions with specific regions in images, a critical skill for AI in robotics, autonomous systems, and content-based retrieval.

Practical examples guide students through implementing semantic search pipelines, using pre-trained models for embeddings, building efficient search indexes, and evaluating retrieval performance. Advanced topics include cross-modal retrieval, fine-tuning models for specific domains, and integrating visual grounding into NLP pipelines for real-world applications.