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.
- 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
Note on Course Availability
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.
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