NLP-Natural Language Processing

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.

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.

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.

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.

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.

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.

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.

Gives students a solid foundation in a specific application area of natural language processing, by learning about the theories, methods, tools, and techniques typically used in this area. The application area varies each quarter, with expected topics to include Information Extraction, Question Answering, Natural Language Generation, Sentiment Analysis, and others. Students learn about the state of the art and open challenges, and have the opportunity to explore and experiment with both standard algorithms and new emerging approaches.

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.

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.

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.

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.

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.

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.