[Syllabus] EECS 595: Natural Language Processing

EECS 595: Natural Language Processing

Instructor: Joyce Chai

Course Description

The field of Natural Language Processing (NLP) is primarily concerned with computational models and computer algorithms to process human languages, for example, automatically interpret, generate, and learn natural language. In the past twenty years, the rise of the world wide web and social media have created tremendous opportunities for exciting NLP techniques and applications. The advances in deep learning have also paved the way to create large-scale language models and tackle many NLP problems in the real world. This course provides an introduction to the state of the art in NLP including large language models, syntax, semantics, discourse, and their applications in information extraction, question answering, and converstional systems.

Text book for Reference


  • Proficiency in Python programming (using NumPy and PyTorch).
  • Knowledge and experience in machine learning.

Schedule of Topics

Text Classification, Logistic Regression
Neural Networks and Backpropagation
Word Vectors and Vector Semantics
Language Modeling
Recurrent Neural Networks
Sequence-to-Sequence Models
Pre-trained Large Language Models
Fine-tuning and Prompting
Constituency Parsing
Dependency Parsing
Meaning Representations
Semantic Parsing
Semantic Roles and Selectional Restriction
Coreference Resolution
Discourse Coherence
Information Extraction
Question Answering
Commonsense Reasoning
Conversational Systems
Advanced Topics

Course Policies

Course Grades

The work in this course consists of four homework assignments and a final project. Each assignment may include a written portion and a programming portion. All homework must be your own work.

Late submission policy

You have up to 3 days after the due date to submit your assignments. After that cut off date, you will receive 0 point. For each day delayed, you will receive a penalty for the assignment. If there is a special circumstance, please contact the instructor/TAs directly.

Academic Honesty

All homework assignments submitted must be your own work. Review the college of Engineering’s Honor Code here: http://www.engin.umich.edu/college/academics/bulletin/rules (Links to an external site.)

Special Accommodations

If you have disabilities or medical conditions which require some form of accommodations, please make an appointment with the instructor within the first week of classes.

Notes: The instructor reserves the right to modify course policies and the course calendar according to the progress and needs of the class.