Syllabus and Logistics



  • understand the significance of commonsense in modern AI
  • master basic knowledge of cognitive science for modern AI
  • derive statistical computational models of commonsense to solve challenging AI problems
  • code and build complex system to model certain aspect(s) of commonsense


  • basic knowledge of statistics, e.g., Bayes rule
  • comfortable with programming in Linux using modern deep learning libraries
  • excellent English skills for reading literature, presentation, and writing homework and reports
  • familiar with LateX for writing homework and reports


  • strict rule: no late submission will be accepted after the due date
  • paper presentation (in English): 20%
  • essay (in English): 3% $\times$ 10 topics = 30%
  • project presentation (in English): 20%
  • project report (in English and LaTeX): 30%

Essay (30%)

  • team: 1 student; work alone
  • duration: 1 week after each lecture
  • topic: choose one from each lecture, 10 in total
  • grading: 3 points per essay
  • delivery: essay (2 pages minimal) in LaTeX, written in English

Paper Presentation (20%)

  • team: 2-4 students
  • duration: up to the presentation session
  • topic: choose papers listed in one of the 10 topics and additional papers at your own choice
  • note: no need to cover all the listed papers, but need to convey a coherent message during the presentation
  • delivery: presentation, in English

Project (50%)

  • team: 2-4 students, usually the same team as for the paper presentation
  • duration: the entire semester
  • topic: choose one listed on the project page (see also the instructions); topics are exclusive among teams; reserve online
  • delivery:
    1. presentation in English
    2. technical report in top conference quality that includes
    • insights of the problem
    • related work / literature review
    • major components of the systems of algorithms
    • technical details, e.g., learning, training, parameters
    • experimental results
    1. publicly available code, e.g., on GitHub



  • please contact the instructor or TA


This course is made possible by a group of collaborators and students

my advisors,

my long-term collaborators,

the most awesome peers in the world (in alphabetical order),

the smartest students/postdocs in the world (in alphabetical order),

and the brilliant artist, Ms. Chen Zhen at BIGAI, who created the cover image and other figures used in this course.

[22530005/04804023] Syllabus


  • Week 01/Lecture 01: Introduction & Logistics

Physical Commonsense

  • Week 02/Lecture 02: Affordance and Functionality
  • Week 03/Lecture 03: Intuitive Physics
  • Week 04/Lecture 04: Causality
  • Week 05/Lecture 05: Tool, Mirroring, and Imitation
  • Week 06/Paper Presentation 1: Physical Commonsense

Social Commonsense

  • Week 07/Lecture 06: Communication
  • Week 08/Lecture 07: Intentionality
  • Week 09/Lecture 08: Animacy and Theory of Mind (ToM)
  • Week 10/Paper Presentation 2: Social Commonsense

Advanced Topics

  • Week 11/Lecture 09: Abstract Reasoning
  • Week 12/Lecture 10: Utility
  • Week 13/Lecture 11: Explainable AI and Teaming
  • Week 14/Paper Presentation 3: Advanced Topics

Project Presentations

  • Week 15/Project Presentation 1
  • Week 16/Project Presentation 2