Investigate the fundamental concepts and ideas in natural language processing (NLP), and get up to speed with current research. Students will develop an in-depth understanding of both the algorithms available for processing linguistic information and the underlying computational properties of natural languages. The focus is on deep learning approaches: implementing, training, debugging, and extending neural network models for a variety of language understanding tasks. The course progresses from word-level and syntactic processing to question answering and machine translation. For their final project students will apply a complex neural network model to a large-scale NLP problem.
- Christopher Manning Professor, Computer Science
- Richard Socher Instructor, Computer Science
- Computational properties of natural languages
- Coreference, question answering, and machine translation
- Processing linguistic information
- Syntactic and semantic processing
- Modern quantitative techniques in NLP
- Neural network models for language understanding tasks
3.0 - 4.0
Students enrolling under the non degree option are required to take the course for 4.0 units.
- Calculus and linear algebra
- CS124, or CS121/CS221