This course examines how contemporary foundation models and Generative AI systems are designed, trained, aligned, adapted, and evaluated. Learners study tokenization, embeddings, transformer internals, pre-training, scaling laws, mixture-of-experts architectures, context windows, instruction tuning, fine-tuning, PEFT, preference optimization, multimodal models, reasoning systems, and test-time compute. The emphasis is on understanding and engineering model behaviour rather than merely consuming hosted APIs.