NLP-Natural Language Processing

NLP 201 Natural Language Processing I

The first course in a series covering the core concepts and algorithms for the theory and practice of natural language processing (NLP), the creation of computer programs that can understand, generate, and learn natural language.

Credits

5

Requirements

Prerequisite(s): Enrollment is restricted to natural language processing graduate students, and computer science and engineering doctoral students by permission of instructor.

Quarter offered

Fall

NLP 202 Natural Language Processing II

This is the second course in a series covering the core concepts and algorithms for the theory and practice of natural language processing (NLP)—the creation of computer programs that can understand, generate, and learn natural language.

Credits

5

Requirements

Prerequisite(s): NLP 201. Enrollment is restricted to natural language processing graduate students.

Quarter offered

Winter

NLP 203 Natural Language Processing III

Third and final course in a series covering the core concepts and algorithms for the theory and practice of natural language processing (NLP)—the creation of computer programs that can understand, generate, and learn natural language.

Credits

5

Requirements

Prerequisite(s): NLP 201 and NLP 202.

Quarter offered

Spring

NLP 220 Data Collection, Wrangling and Crowdsourcing

Covers a broad set of tools and core skills required for working with Natural Language Data. It covers methods for collecting, merging, cleaning, structuring and analyzing the properties of large and heterogeneous datasets of natural language, in order to address questions and support applications relying on those data. Also covers both working with existing corpora as well as the challenges in collecting new corpora.

Credits

5

Requirements

Enrollment is restricted to natural language processing graduate students.

Quarter offered

Fall

NLP 243 Machine Learning for Natural Language Processing

Introduction to machine learning models and algorithms for natural language processing (NLP) including deep learning approaches. Targeted at professional master's degree students, course focuses on applications and current use of these methods in industry. Topics include: an introduction to standard neural network learning methods such as feed-forward neural networks; recurrent neural networks; convolutional neural networks; and encoder-decoder models with applications to natural language processing problems such as utterance classification and sequence tagging.

Credits

5

Requirements

Enrollment is restricted to natural language processing graduate students.

Quarter offered

Fall

NLP 244 Advanced Machine Learning for Natural Language Processing

Introduces advanced machine learning models and algorithms for Natural Language Processing. Theoretical and intuitive understanding of NLP learning models will be discussed. Some hot topics such as robustness and explainability in ML for NLP will also be covered. The course assumes that students have taken NLP 243, graduate level machine learning.

Credits

5

Requirements

Prerequisite(s): NLP 243. Enrollment is restricted to natural language processing graduate students and computer science and engineering doctoral students, or by permission of instructor.

Quarter offered

Winter

NLP 245 Conversational Agents

Reviews recent work on conversational AI systems for task-oriented, informational, and social conversations with machines. Students read and review theoretical and technical papers from journals and conference proceedings, lectures and engage in discussions. The course assumes that NLP 243, NLP 201 and NLP 202 or equivalent have already been completed. A research project is required.

Credits

5

Requirements

Prerequisite(s): NLP 243, or CSE 242, or CSE 243. Enrollment is restricted to natural language processing graduate students and computer science and engineering doctoral students, or by permission of instructor.

Quarter offered

Winter, Spring

NLP 267 Machine Translation

Machine Translation systems can instantly translate between any pair of over eighty human languages such as German to English or French to Russian. Modern translation systems learn to translate by reading millions of words of already translated text. This course covers the models and algorithms used by such systems and explains how they are able to automatically translate one human language to another. The course covers fundamental building blocks using concepts from linguistics, statistical and deep machine learning, algorithms, and data structures. It provides insight into the challenges associated with machine translation and introduces novel approaches that might lead to better machine translation systems.

Credits

5

Requirements

Prerequisite(s): NLP 201; and NLP 243 or CSE 244. Previous or concurrent enrollment in NLP 202. Enrollment is restricted to natural language processing graduate students and computer science and engineering Ph.D. students, or by permission of instructor.

Quarter offered

Winter, Spring

NLP 270 Linguistic Models of Syntax and Semantics for Computer Scientists

Provides an introduction to theoretical linguistics for natural language processing, focusing on morphology, syntax, semantics, and pragmatics, and on training students in linguistic description, representation, and argumentation. Students learn to describe common features underlying natural languages and to manipulate several syntactic and semantic representations.

Credits

5

Requirements

Enrollment is restricted to natural language processing graduate students and computer science and engineering Ph.D. students, or by permission of instructor.

Quarter offered

Winter, Spring

NLP 271A Capstone I: Natural Language Processing

The first in a sequence of three capstone courses providing hands-on practice of key NLP concepts and skills and experience working in a team project setting. Provides students with tools for project management and working in a team. Explores multiple possible projects, and methods for presenting projects, and investigates what makes a good project proposal, and how to evaluate and understand the strengths and weaknesses of project proposals.

Credits

3

Requirements

Prerequisite(s): NLP 201 and NLP 220. Enrollment is restricted to graduate students.

Quarter offered

Winter

NLP 271B Capstone II: Natural Language Processing

The second in a sequence of three capstone courses providing hands-on practice of key NLP concepts and skills and experience working in a team project setting. Provides students with tools for project management and working in a team. The course allows students to refine and further define several possible projects, present them for feedback, produce initial implementations of key modules and assess them, culminating in the writing and presentation of a detailed final project proposal along with its initial implementation.

Credits

5

Requirements

Prerequisite(s): NLP 202 and NLP 271A. Enrollment is restricted to graduate students.

Quarter offered

Spring

NLP 271C Capstone III: Natural Language Processing

The third in a sequence of three capstone courses providing hands-on practice of key natural language processing concepts and skills and experience working in a team project setting. This course provides students with tools for project management and working in a team .Focuses on completing the capstone project, by working together as a team, implementing the project in phases, testing and assessing the quality of the final implementation, writing the final project report, and oral presentation of the team project.

Credits

5

Requirements

Prerequisite(s): NLP 203 and NLP 271B. Enrollment is restricted to graduate students.

Quarter offered

Summer

NLP 280 Seminar in Natural Language Processing

Weekly seminar course covering current research and advanced development in all areas of Natural Language Processing. The seminar is based on invited talks by guest speakers from industry research and advanced development working in the area of Natural Language Processing. Students attend talks given by speakers in a weekly seminar series and participate in discussions after the talks. This class can be taken for Satisfactory/Unsatisfactory credit only.

Credits

2

Requirements

Enrollment is restricted to natural language processing graduate students and computer science and engineering doctoral students. Others may enroll by permission of instructor.

Repeatable for credit

Yes

Quarter offered

Fall, Winter, Spring

Cross-listed courses that are managed by another department are listed at the bottom.

Cross-listed Courses