Office: 901 Milstein (Barnard Campus)
Office hours: Monday 12:00-1:00pm
Email: [email protected]
Annette Antony
Office: CS Help Room (Milstein 502)
Office hours: Mondays 9:00am-10am, Tuesday 9-10am
Email: [email protected]
Hamsitha Challagundla
Office: CS Help Room (Milstein 502)
Office hours: Thursdays 1-2pm and Fridays 9-10am
Email: [email protected]
Puja Singla
Office: CS Help Room (Milstein 502)
Office Hours: Tuesdays 6-7pm; Wednesday 12-1pm
Email: [email protected]
Office: CS Help Room (Milstein 502)
Office hours: Wednesday 3-5pm
Email: [email protected]
Role of a computing fellow: Computing Fellows are peer leaders / mentors who work to promote students’ engagement, skills, and interest in computing. They can provide one-on-one mentoring in office hours and asynchronously, but they are NOT involved in grading! (only TAs and instructor will be involved in grading)
This course provides an introduction to the field of Natural Language Processing (NLP) at an undergraduate level. We will discuss properties of human language at different levels of representation (morphology, syntax, semantics, pragmatics), and will learn how to create systems that can analyze, understand, and generate natural language. We will study machine learning methods used in NLP such as various forms of Neural networks and will focus particularly on conceptual and technical advances of frontier Large Language Models based NLP technologies (think ChatGPT) that are revolutionizing classical computational linguistics and NLP fields. We will also discuss applications such as question answering, summarization, language generation and as well as data, benchmarks and evaluation frameworks. We will discuss ethical aspects of NLP research and applications. Homework assignments will consist of programming projects in Python. Class will also have a midterm and a mini final project instead of a final exam.
Prerequisite(s): COMS W3134 or W3136 or W3137 (or equivalent). Background in probability/statistics and linear algebra is also required and experience with Python programming is strongly encouraged. Some previous or concurrent exposure to AI and machine learning is beneficial, but not required.
[09/29/2025]: This week office hours for instructor Wed 12-1pm. Updated schedule + Assignment 2 timeline
[09/18/2025] Instructor office hours moved to Mondays 12-1pm
Due 09/26/2025 11:59pm EST] Assignment 1 due
Due 09/05/2025 11:59pm EST] Please make sure you fill in the First Class Survey (even if you filled the waitlist survey). This is required (if you will not fill you will lose points on class participation). We will use this information to learn about your background to tailer material for class.
Some topics/readings might be subject to change