Course Details

Department of Electrical and Computer Engineering
University of Illinois at Urbana-Champaign
Instructor: Prof. Suma Bhat

Lectures (All US Central Time):

• 9:30 AM - 10:50 PM TR

• In-person (from the second week) in ECEB 4070

Course Zoom link (until further notice)

Gradescope entry code 4PK48N

And don't forget to add yourself on Piazza!

Course Outline

Language technologies using AI and NLP are at work in our daily lives in tasks ranging from grammatical error correction to machine translation to online question answering. The intricacies of the language we use as part of our everyday activities pose distinct challenges for computers that process unstructured text in the absence of real-word interaction context or intent. In this course, we understand and analyze classical and recent computational models for addressing these challenges. The course will be centered around the following broad themes each over 5 weeks.

• Modeling language-related properties

• Applications of language processing

• Computational social science

We cover each of these themes in depth, discussing the core aspect of the theme via lectures and supplementing this with discussions using key papers on each topic. Students are expected to contribute the the discussion by way of weekly readings and presentations of research papers.

Course Objectives

• To give the student a feel for the area of natural language processing by understanding the core challenges and computational models for processing language and current methods of cutting-edge research.

• To enable the student to develop critical research skills, through literature review in the area, organize and share ideas via oral and written presentations as well as providing constructive feedback to peers.

Prerequisites

• Basic Probability and Statistics 

• Foundations of Machine Learning (e.g. CS 446 or equivalent material)

  We will rely on several concepts typically seen in machine learning content. If you need to brush them up with concurrent efforts, you may consult any of the excellent introductions to ML, some of which are listed in the Reference Books section. 

• Proficiency in Python
  All course assignments will be in Python. If you think you need a refresher, please do so yourself well in advance before the assignments.