MIS 401 Deep Learning and Large Language Models for Business Analytics

Recent large language models (LLM) have demonstrated unprecedented potentials and capabilities. This course aims to provide technical fundamentals of deep learning for students to understand these models and leverage them to create innovative business applications. This course progressively introduces topics from the basics to state-of-the-art systems, including: Basic building blocks: feedforward networks, gradients and back propagation, model optimization and regularization. Computer vision: convolutional neural networks and variants, autoencoder, and generative models. Language models: Fundamentals of language understanding and generation: recurrent networks, attention mechanisms, sequence-to-sequence models. State-of-art large language models (Transformer, BERT, GPT): technical foundations, emerging capabilities (knowledge, reasoning, few-shot learning, in-context learning), as well as security and ethics.

Credits

3

Prerequisite

BT 366 and QF 301

Distribution

School of Business

Typically Offered Periods

Fall Semester Spring Semester