Introduction to Natural Language Processing

This NLP course explores AI's capability to understand human language, focusing on machine learning algorithms and large language models. It covers language modeling, sentiment analysis, tagging, and machine translation, discussing lexical, syntactic, and semantic processing. Key models include naive Bayes, logistic regression, and neural networks like RNNs and CNNs. It emphasizes empirical analysis of text corpora and how to evaluate the strengths and limitations of statistical tools, including large language models, in NLP.

No required prerequisites. Recommended prerequisites:

• Good programming skills and knowledge of data structure (e.g., CS 130A)
• Basic understanding of automata and parsing (e.g., CS 138)
• Advanced knowledge in machine learning (CS 165B), artificial intelligence (CS 165A), linear algebra, probability, and calculus.

​​​​​​​Enrollment restrictions: Open to CS/CE majors