Text Processing Course: Introduction to Natural Language Processing

Deep Learning Specialization on Coursera

Name: Introduction to Natural Language Processing

Website: http://people.cs.umass.edu/~brenocon/inlp2015/


Natural Language Processing (NLP) is the engineering art and science of how to teach computers to understand human language. NLP is a type of artificial intelligence technology, and it’s now ubiquitous – NLP lets us talk to our phones, use the web to answer questions, map out discussions in books and social media, and even translate between human languages. Since language is rich, subtle, ambiguous, and very difficult for computers to understand, these systems can sometimes seem like magic – but these are engineering problems we can tackle with data, math, machine learning, and insights from linguistics.

This course will introduce NLP methods and applications including probabilistic language models, machine translation, and parsing algorithms for syntax and the deeper meaning of text. During the course, students will (1) learn and derive mathematical models and algorithms for NLP; (2) become familiar with key facts about human language that motivate them, and help practitioners know what problems are possible to solve; and (3) complete a series of hands-on projects to implement, experiment with, and improve NLP models, gaining practical skills for natural language systems engineering.

This course is intended for upper-level CS undergraduates and graduate students, as well as linguistics students with an appropriate background.

Prerequisites: experience in programming and probability. Undergraduates must have completed:
((CS220 or CS230) and CS240) or Ling492B).

About the Instructors

Brendan T. O’Connor

“I am an assistant professor in the College of Information and Computer Sciences at University of Massachusetts, Amherst (since Fall 2014). I am affiliated with the Computational Social Science Institute, the Centers for Data Science and Intelligent Information Retrieval, and the Machine Learning for Data Science lab.”

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