Course Description
This course provides an introduction to the field of natural language processing (NLP). We will learn how to create systems that can analyze, understand and produce language. We will begin by discussing core NLP, such as language modeling, part of speech tagging and parsing. We will also discuss applications such as information extraction, machine translation, automatic summarization, and question-answering. The course will primarily cover statistical and machine learning based approaches to language processing, but it will also introduce the use of linguistic concepts that play a role. We will study machine learning methods currently used in NLP, including supervised machine learning, hidden markov models, and neural networks. Homework assignments will include both written components and programming assignments.
This website is currently being revised for Fall 2019. Stay tuned. A new syllabus and details will become available in several weeks.
IMPORTANT DETAILS ABOUT GETTING INTO THE CLASS: There will be seats for about 100 in the class. There are many people who want to take the class and who are on the waitlist. Jessica Rosa (rosa@cs.columbia.edu) will be determining who is placed in the class. CS majors and CS graduate students are given first preference. Given the number of people who want to take the course, it seems unlikely to me that people outside of CS will get in. Please email Jessica if you have questions. I will not be answering emails about getting into the class since it's outside of my control.
Four homework assignments, a midterm and a final exam. Each student in the course is allowed a total of 4 late days on homeworks with no questions asked; after that, 10% per late day will be deducted from the homework grade, unless you have a note from your doctor. Do not use these up early! Save them for real emergencies.
We will use Google Cloud for the course. Instructions for setting up the cloud can be found here.