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This course provides a broad foundation for understanding, working with, and adapting existing tools and technologies in the area of Large Language Models like BERT, T5, GPT, and others.

Throughout this course, you will learn:

• A range of topics including systems, data, data filtering, training objectives, RLHF/instruction tuning, ethics, policy, evaluation, and other human-facing issues.
• How transformer architectures work and why they are better than LSTM-based seq2seq models. You鈥檒l explore decoding strategies and techniques for pretraining, attention, prompting, and more through readings and hands-on assignments.
• How to apply the skills you鈥檝e learned in a semester-long course project, using locally sourced model instances that allow you to go beyond commercial APIs.
• How to compare and contrast different models in the LLM ecosystem to determine the best model for a given task.
• How to implement and train a neural language model from scratch using PyTorch.
• How to utilize open source libraries to fine-tune and perform inference with popular pre-trained language models.
• How to apply LLMs in downstream applications and understand how pre-training decisions affect task suitability.
• How to design new methodologies that leverage existing large-scale language models in novel ways.

Please note: In order to complete homework and activities, students will need to sign up for Amazon Web Services (AWS) or an equivalent service that offers access to A10g or similar GPUs. The AWS cost to complete assignments will range from $150鈥�$300, depending on usage. Additionally, students will need to sign up for the OpenAI API. The cost to complete the assignments via OpenAI will be up to $25. Instructions for accessing both services will be provided at the start of the course.

In this course, you will learn the fundamental mathematical concepts in machine learning and deep learning that are relevant to the five main challenges in multimodal machine learning:

• Multimodal representation learning
• Translation and mapping
• Modality alignment
• Multimodal fusion
• Co-learning

The mathematical concepts you will learn include, but are not limited to, multimodal auto-encoders, deep canonical correlation analysis, multi-kernel learning, attention models, and multimodal recurrent neural networks.

You will also review recent papers describing state-of-the-art probabilistic models and computational algorithms for multimodal machine learning and discuss current and emerging challenges. Finally, you will study recent applications of multimodal machine learning including multimodal affect recognition, image and video captioning, and cross-modal multimedia retrieval.

Please note: The Multimodal Machine Learning course may require Amazon Web Services (AWS) and/or OpenAI or other services to complete assignments, with fees up to $300 (subject to change). More details will be available as you get closer to the course start date.

LLMs are often very large and require increasingly larger datasets to train, which means developing scalable systems is critical for advancing AI. In this course, you will learn the essential skills for designing and implementing scalable LLM systems.

Throughout the course, you will:

• Learn the approaches for training, serving, fine-tuning, and evaluating LLMs from the systems perspective.
• Gain familiarity with sophisticated engineering using modern hardware and software stacks needed to accommodate the scale.
• Acquire essential skills for designing and implementing LLM systems, including:
  • Algorithms and system techniques to efficiently train LLMs with huge data
  • Efficient embedding storage and retrieval
  • Data-efficient fine-tuning
  • Communication-efficient algorithms
  • Efficient implementation of reinforcement learning with human feedback
  • Acceleration on GPU and other hardware
  • Model compression for deployment
  • Online maintenance
• Learn about the latest advances in LLM systems regarding machine learning, natural language processing, and systems research.

Please note: The Large Language Model Systems course may require Amazon Web Services (AWS) and/or OpenAI or other services to complete assignments, with fees up to $300 (subject to change). More details will be available as you get closer to the course start date.