CS 283P Natural Language Processing Applications
This course will provide a comprehensive introduction to Natural Language Processing (NLP) with a focus on applications. Students will explore both traditional NLP techniques and modern deep learning approaches, including neural network-based architectures like RNNs, LSTMs, GRUs, and modern attention-based architectures like Transformers. The course will conclude with the exploration of Large Language Models (LLMs) and multimodal models, addressing the latest advances in NLP. By engaging in hands-on assignments and a final project, students will gain practical experience in developing NLP models for real-world applications. Emphasis will be placed on understanding model interpretability, prompt engineering, and the potential of NLP for AI-driven applications in diverse fields in the final part of the course.
Prerequisite
This course will build on top of the concepts of machine learning course. The students should be aware of basics of neural networks, forward/backward pass, basic optimziation and Python Programming.