Syllabus and Logistics

Syllabus

Introduction

• Week 01/Lecture 01 (09.09): Introduction & Logistics

Physical Commonsense

• Week 02/Lecture 02 (09.14): Affordance, Functionality, and HOIs
• Week 03/Lecture 03 (09.23): Intuitive Physics and Causality (Part 1)
• Week 04/Lecture 04 (09.30): Causality (Part 2), Tool, Mirroring, and Imitation
• Week 05: National Holidays
• Week 06/Paper Presentation 1 (10.14): Physical Commonsense (Attendance Required)

Social Commonsense

• Week 07/Lecture 05 (10.21): Communication and Language
• Week 08/Lecture 06 (10.28): Intentionality, Animacy, and Theory of Mind (ToM)
• Week 09/Lecture 07 (11.04): Social Learning, Guest Lecture by Lifeng Fan (BIGAI)
• Week 10/Lecture 08 (11.11): Game (Attendance Required)
• Week 11/Paper Presentation 2 (11.18): Social Commonsense (Attendance Required)

Advanced Topics

• Week 12/Lecture 09 (11.25): Abstract Reasoning, Guest Lecture by Chi Zhang (BIGAI)
• Week 13/Lecture 10 (12.02): Explainable AI and Teaming, Guest Lecture by Zilong Zheng (BIGAI)
• Week 14/Paper Presentation 3 (12.09): Advanced Topics (Attendance Required)

Project Presentations

• Week 15/Project Presentation 1 (12.16) (Attendance Required)
• Week 16/Project Presentation 2 (12.23) (Attendance Required)
• Week 17/Project Presentation 3 (12.29) (Attendance Required)

Logistics

Objectives

• 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

Prerequisites

• 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

Grading

• strict rule: no late submission will be accepted after the due date
• paper presentation (in English): 20%
• essay (in English): 3% $\times$ 5 topics = 15%
• attendance: 15% for attending presentations; you will automate fail if you miss more than twice.
• project presentation (in English): 20%
• project report (in English and LaTeX): 30%

Essay (15%)

• team: 1 student; work alone
• duration: 1 week after each lecture
• topic: choose one from each lecture; you will need to submit at least 5
• grading: 3 points per essay
• delivery: essay (2 pages minimal) in LaTeX, written in English

Paper Presentation (20%)

• team: 4-6 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: 4-6 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
  3. publicly available code, e.g., on GitHub

Plagiarism

• please refer to the plagiarism page for details.

Errata/Typo

• please contact the instructor or TA

Acknowledgement

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