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Natural Language Processing Fall 2026

Announcement (Updated Sept 29)

Hi class,

The reformation, taking the inputs from students and previous instructors, after the meeting with our teaching team and majority vote, is as below:

  1. The quiz will be discontinued. Its 10% grade moves to 5% each for midterm and final. Please use the conceptual assignments to review the course materials.
  2. quiz 0: in the first class, no grading. quiz 1 (1 score) and quiz 2 (1 score), the grades will be bonus grades. The bonus scores are added AFTER the curve. That means, if you lost these two bonus scores, but you obtained 100 scores for the rest of the tasks, you will have 100 scores after the curve.
  3. 10% engagement changes to 10% in-person attendance.
  4. We only use main website and Coursys (all submissions go to here) from now on. I.e., we discontinue the usage of Gradescope and Canvas. The Deadline and course information in Coursys will synchronize with the main website.
  5. We will try to provide sample exams for midterm and final exam. A calculator, one page (double sided) cheat sheet, are allowed. Final exam date is not determined.
  6. Regarding “weekly online quizzes that allow unlimited attempts”, I like this idea, but unfortunately the teaching team has voted against it. I may bring this to the next NLP course design. -In the meantime, you can still make use of the AI to design the “quizzes that allow unlimited attempts”, to achieve the learning effect. Also, the small questions I asked in class, and shown in the slides, can also be the self-evaluation quizzes.

The basic idea behind these changes is: a) Enforce the fairness; b) Keep the grading rubric stable after this reformation. The reformation may not satisfy everyone, but we have tried our best to make the decision.

Take a good rest on Sept 30th’s holiday and see you on Oct 2nd,
Richard

Imagine a world where you can pick up a phone and talk in English, while at the other end of the line your words are spoken in Chinese. Imagine a computer animated representation of yourself speaking fluently what you have written in an email. Imagine a computer writing new poetry and stories from a prompt or generating art based on descriptions. Imagine automatically uncovering protein/drug interactions in petabytes of medical abstracts. Imagine feeding a computer an ancient script that no living person can read, then listening as the computer reads aloud in this dead language. Imagine a computer that can do better than humans at answering questions.

Natural Language Processing is the automatic analysis of human languages such as English, Korean, and thousands of others analyzed by computer algorithms. Unlike artificially created programming languages where the structure and meaning of programs is easy to encode, human languages provide an interesting challenge, both in terms of its analysis and the learning of language from observations.

Instructor

Teaching Assistants

  • Nima Forouzi, nfa23, Office hour: Thursdays 6 to 7 PM on [Zoom](https://sfu.zoom.us/j/83421743065).
  • Borui Li, bla127, Office hour: Thursdays 6 to 7 PM on [Zoom](https://sfu.zoom.us/j/83421743065).
  • Qinchan Li, qla126, Office hour: Thursdays 6 to 7 PM on [Zoom](https://sfu.zoom.us/j/83421743065).

Asking for help

  • Ask for help on Coursys Discussion Forum
  • Instructor office hours: On Wednesdays from sept 16 to Nov 29, at 11 - 12 PM, at TASC1 9409, except national holidays.
  • No emails to the TAs and strictly emails about personal matters to the instructor
  • Use only SFU email address and use either cmpt413: orcmpt713: as subject prefix
  • Always post to the Coursys Discussion Forum instead of email. If you have to email use your SFU email address only.

Time and place

  • Sep 9 – Dec 6, 2026: Wed, 1:30–2:20 p.m.
  • Sep 9 – Dec 6, 2026: Fri, 12:30–2:20 p.m.
  • location : TBD, Burnaby campus.
  • Last day of classes: Dec 6, 2026

Links to course material will be made available on Coursys.

Prerequisites

There are no formal prerequisites for this class. However, you are expected to be familiar with the following:

  • Proficiency in Python - Programming assignments will be in python (numpy and pytorch will be used).
  • Calculus and Linear Algebra (MATH 151, MATH 232/240) - You will need to be comfortable with taking multivariable derivatives
  • Basic Probability and Statistics (CMPT 210 or STAT 270)
  • Basic Machine Learning (CMPT 410/726) is strongly recommended (Note: CMPT 410 was previously offered as CMPT 419 under the title “Machine Learning”)

There will be optional TA led tutorials that will help review these topics.

Textbook

Grading

  • Submit all homework assignments and check your grades on Coursys. We have discontinued the usage of Gradescope and Canvas. Deadlines and course information in Coursys will synchronize with the main website.
  • Programming setup and diagnostic homework (4%)
    • HW0 due on Sep 18, 2026
  • Four homeworks (16% total - 4% each). Due dates:
    • HW1 on Oct 7, 2026
    • HW2 on Oct 23, 2026
    • HW3 on Oct 30, 2026
    • HW4 on Nov 20, 2026
  • Midterm Exam (20%)
  • Final Exam (25%)
  • Final Project (25% total)
    • Project Proposal: Due on Oct 28, 2026 (3%)
    • Project Milestone: Due on Nov 25, 2026 (5%)
    • Project “Poster” Presentation: Poster due on Dec 4, 2026 (5%)
    • Project Report and Code: Due on TBD (12%)
  • Participation (10%): In-person attendance.
  • Bonus: Quizzes 1 and 2 (1 point each) will be added to your final score as bonus points outside the standard grading scheme. The bonus scores are added AFTER the curve. Please use the conceptual assignments to review the course materials.

Exams

  • We will try to provide sample exams for midterm and final exam.
  • A calculator and one page (double sided) cheat sheet are allowed.
  • Final exam date is not determined.