Simon Fraser University
Engaging the World

Course Outlines

Fall 2021 - CMPT 983 G100

Special Topics in Artificial Intelligence (3)

Class Number: 5636

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 8 – Dec 7, 2021: Tue, 2:30–4:20 p.m.
    Burnaby

    Sep 8 – Dec 7, 2021: Fri, 2:30–3:20 p.m.
    Burnaby

Description

COURSE DETAILS:

Graph data represent relationships between entities in a domain. They are a common data type, which makes them important for many applications. Domains with major graph datasets include the following: enterprise data management through relational databases, social networks, bioinformatics (e.g. protein-protein interactions), information extraction in natural language processing, where knowledge graphs represent a large amount of information that can be extended through on-line sources. While graph data are powerful and widely available, they are a challenge for standard machine learning methods that are designed for independent data points. The goal of this course is to introduce students to the special challenges of learning from graph data, and to the machine learning methods that have been developed to address them. Many recent approaches are based on deep learning, because neural methods provide accurate predictions and are relatively easy to implement. The course will therefore emphasize graph neural networks. The course is an in-person seminar course, which means that I expect strong participation from students. I will give lectures to fill in background but as much as possible classes will be interactive. About half the class will be devoted to background on previous machine learning methods for graph data, and about half to discussing advanced topics, current research papers, and project ideas.

COURSE-LEVEL EDUCATIONAL GOALS:

Topics

  • Types of Graph Data: Homogeneous, Heterogeneous, Multi-Relational
  • Traditional Methods: Node Features, Graph Kernels, Spectral Analysis, Exponential Random Graph Model
  • Node Embeddings
  • Graph Neural Networks
  • Graph Generative Models
  • Advanced Topics from current research (methodology and applications)

Grading

NOTES:

* Exercises/Quizzes: 10%. • Background/Topic Presentation: 30%. • Project Presentation 15% • Final Project 45%

Materials

REQUIRED READING:

  • Graph Representation Learning, William Hamilton, Morgan and Claypool, 2020, https://www.cs.mcgill.ca/~wlh/, The free electronic preprint version is sufficient for this course.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN FALL 2021

Teaching at SFU in fall 2021 will involve primarily in-person instruction, with approximately 70 to 80 per cent of classes in person/on campus, with safety plans in place.  Whether your course will be in-person or through remote methods will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.