3 credits
Fall 2025 Lecture Distance Learning Upper Division
This course will cover the key concepts and methods used in modern Natural Language Processing (NLP). Throughout the course several core NLP tasks, such as sentiment analysis, information extraction, syntactic and semantic analysis, will be discussed. The course will emphasize machine-learning and data-driven algorithms and techniques, and will compare several different approaches to these problems in terms of their performance, supervision effort and computational complexity. Prerequisites: A background in linear algebra, calculus, statistics and probability, and completion of CS 57800 or equivalent are highly recommended. Strong programming skills in any modem language (Python, Java, C++) are required.
Learning Outcomes1Describe and analyze the key challenges in dealing with natural language data and other fundamental areas of NLP.
2Analyze and implement the key algorithms and techniques used in NLP.
3Identify algorithmic techniques that can be applied to new problems and evaluate other possible solutions.
4Conduct experiments using proper methodology for training and testing NLP systems using data.
5Critically review current research work in the NLP field.
Restrictions
made by Purdue students.