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Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

This course is an in-depth study of advanced machine learning algorithms used in the current development of large language models (LLMs). The course covers a wide range of topics, starting with mathematical models for sequence generation, and important neural network architectures with a focus on transformers. We will examine issues such as context length, explainability, optimization strategies, and variants of transformer models for image generation and understanding. We will then investigate variants of transformer based language models, along with algorithms for prompt engineering and improving reasoning capability. Other topics include ML techniques used in studying LLM safety, hallucination, fine-tuning of LLMs, alignment (reinforcement learning from human feedback), multimodal LLMs, and common methods for accelerating training and inference. Prerequisites: This course focuses on the understanding of machine learning algorithms in the development of LLMs, rather than applicati