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Natural Language Processing Fall 2020

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 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

  • Ali Gholami, gholami, Office hour: Time: Mon 3-4pm.
  • Sonia Raychaudhuri, sraychau, Office hour: Time: Thurs 3-4pm.
  • Yue Ruan, yuer, Office hour: Time: Tues 3-4pm.

Asking for help

  • Ask for help on piazza
  • Instructor office hours: Time: Wed 2-3pm
  • No emails to the TAs and strictly emails about personal matters to the instructor
  • Use only SFU email address and use either cmpt413: orcmpt825: as subject prefix

Time and place

Course lectures will be held using canvas BB Collaborate Ultra

  • Wed 11:30am-12:20pm Online
  • Fri 10:30am-12:20pm Online
  • Last day of classes: Dec 4, 2020

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 (STAT 270)
  • Basic Machine Learning (CMPT 419/726) is strongly recommended

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

Textbook

Grading

  • Submit homework source code and check your grades on Coursys
  • Programming setup and diagnostic homework (2%)
    • HW0 due on Sept 16, 2020
  • Four homeworks (60% total - 15% each, with 10% for programming and 5% for question answering). Due dates:
    • HW1 on Sept 30, 2020
    • HW2 on Oct 14, 2020
    • HW3 on Oct 28, 2020
    • HW4 on Nov 11, 2020
  • Final Project (35% total)
    • Project Proposal: Due on Oct 23, 2020 (5%)
    • Project Milestone: Due on Nov 20, 2020 (5%)
    • Project “Poster” Presentation: Online on Dec 4, 2020 (5%)
    • Project Report and Code: Due on Dec 8, 2020 (20%)
  • Participation: Helping other students on the discussion board in a positive way (3%)