CSCI 444 Spring 2026: NLP

🐝 Spring 2026 ⏰ Mon / Wed 10:00 - 11:50a 📍 DMC 151

Announcements

See Brightspace.
Syllabus is here, see below for most updated schedule.

Summary

This class is all about language models: the fundamentals, spanning from simple architectures to modern Transformer-based neural architectures underlying large language models.

Calendar + Syllabus

This calendar is subject to change. More details, e.g. lecture slides will be added as the semester continues. All work (except the project final report) is due on the specified date by 11:59 PM PT. See the syllabus for more details.

Assignments and Grading

There will be three components to course grades:

• Homeworks (18%).
  • 6% X 3: There will be three coding homework assignments based on the topics of the class.
• Semester Project (55%).
  • Each student will do a group class project based on the topics covered in the class. Students will propose their own project, do the research and build a proof-of-concept, create a demonstration (e.g. video) of the proof-of-concept, and present the project in their report.
  • Proposal + Pitch: 10%
  • Midterm Report: 15%
  • Final Project Presentation: 15%
  • Project + Final Write-up: 15%
• Paper Review (12%).
  • Students will write a research paper review to explain concepts underlying natural language processing in their own words (Learning Objective O2) and present the paper in class. The course explores topics through a series of assigned readings in the form of research papers and book chapters. Also, the semester project would require a literature review. Students will select one reading option and submit a two-page summary of that reading and present the papers in teams of 3 and drive discussion in the class. Reviews will be assessed based on answering a small set of questions, to be released at the time of the paper assignment, clearly and correctly. In most cases, each question will warrant at minimum a paragraph to answer.
• Class Participation (15%)
  • Each student’s engagements in course discussions during class and during project discussions.

All written assignments related to the final project should use the standard *ACL paper submission template.

Late Days

The course will allow for a budget of 5 Late Day Tokens per student. These tokens can be expended on homeworks, the paper review, and project deliverables (NOT presentations or final project report) to extend the deadline, one day at a time, for a student without incurring a late penalty. These tokens should be used with no justification or explanation for taking the late time required (i.e., you do not need to explain your reason). Going over budget (e.g., turning things in late with no Late Day Tokens to expend) will incur grade penalties of 5% per day late. To ensure reasonable grading turnarounds and discussions of solutions, any assignment turned in 8 days late or more will receive an automatic zero regardless of the use of Late Day Tokens. For project teams, Late Day Token expenditures are on a per-student basis (i.e., if a team of 2 turns in their midterm report one day late, a member expending a Late Day Token will receive a 0% late penalty, while a member not expending a Late Day Token will receive a 5% late penalty). There are no refunds for late days: unused late days cannot be converted into credit of any kind.

Note: Please familiarize yourself with the academic policies and read the note about student well-being.

Pre-Requisites

Students are required to have taken

• CSCI 170 and
• 1 from (CSCI 104 or CSCI 114) and
• 1 from (MATH 225 or EE 141) and
• 1 from (EE 364 or MATH 407 or BUAD 310 or ISE 225) Recommended Preparation: Fluency with Python programming on the level of ITP 216 or TAC 216