Syllabus:
Initiatives such as Amazon Mechanical Turk and OpenStreetMaps have proven that crowd-sourcing and human-computing techniques are valuable ways to both analyze and annotate large datasets that require human expertise as well as to solve problems that are difficult to treat with classical computer algorithms. Distributed computing platforms such as a SETI@home or Scientific games like Galaxy Zoo, Foldit and Phylo illustrate the potential of these techniques for studying, mining, and processing scientific data.
This course aims to provide an introduction to the emerging field of human computation and to discuss its impact on the scientific discovery processes. We will study existing platforms, review related theoretical frameworks, and study the impact of this technology in the field of artificial intelligence. Finally, we will discuss the perspectives offered by new mobile computing technologies.

Instructor:

• Prof. Jérôme Waldispühl (jerome.waldispuhl@mcgill.ca).

Lectures:

• Tuesday & Thursday, 13:05am to 14:25pm in Mc Connell Eng. Building 103.

Office hours:

Prof. Jérôme Waldispühl

Thursday 14:30-16:00

ENGTR 3106

Course Webpage: http://www.cs.mcgill.ca/~jeromew/comp766.html

Course Material:
All the material needed for this class will be available on the public course web page. There is no required textbook. Although, we recommend the following textbooks to deepen the material presented in class:

• [LVA2012] Edith Law and Luis von Ahn Human Computation: An Integrated Approach to Learning from the Crowd, Morgan & Claypool Publishers, 2012.
• [N2011] Michael Nielsen Reinventing discovery: The new era of networked science, Princeton University Press, 2011.

Lecture slides will be made available in PDF form on the course web page.

Evaluation
Your final grade will be calculated as follows:

• 60% for the project report
• 30% for two paper presentations (15% each)
• 10% for class participation

Release Date	Due Date	Announce
Nov 7	Nov 16	A description of the projects including task assignment, description and timeline is now available.
Oct 26	Oct 27	A memo to prepare the class project is now available.
Oct 18	Oct 20	Class of Oct 20th is cancelled. Replacement will be announced on a later date.
Oct 16		The project assignments is now available.
Oct 4	Oct 9	The project selection form is now available
Sep 27		Paper to review are available HERE
Sep 6		Welcome to COMP766!

	Date	Topic	Material
Lecture 1	Sep 6	Syllabus. Introduction to Human Computation	[Slides]
Lecture 2	Sep 8	Designing Human-computation Algorithms	[Slides] Chapter 2 of [LVA2012]
Lecture 3	Sep 13	Aggregating outputs	[Slides] Chapter 3 of [LVA2012]
Lecture 4	Sep 15	Routing human computation tasks	[Slides] Chapter 4 of [LVA2012]
Lecture 5	Sep 20	Know your audience	[Slides] Chapter 5 of [LVA2012]
Lecture 6	Sep 22	The Art of Asking Questions	[Slides] Chapter 6 of [LVA2012]
Lecture 7	Sep 27	Presentation of projects	[Slides]
Lecture 8	Sep 29	Case study: Phylo (part 1)	[Slides]
Lecture 9	Oct 4	Case study: Phylo (part 2)
Lecture 10	Oct 6	Invited talk: Pascal Nataf, Affordance Studio
Lecture 11	Oct 11	Invited talk: Olivier Tremblay-Savard, University of Manitoba.	[Slides]
Lecture 12	Oct 13	Student paper presentation Rudolf Lam: Accounting for Taste: Ranking curators and content in social networks Mansha Imtiyaz: Toward a Learning Science for Complex Crowdsourcing Tasks
Lecture 13	Oct 18	Student paper presentation Patrick Sejour Chassagne: Supporting Collaborative Writing with Microtasks Kininyiruchi Echomgbe: Curiosity Killed the Cat, but Makes Crowdwork Better Faizy Ahsan: Citizens for Science and Science for Citizens: The View from Participatory Design
Lecture 14	Oct 25	Student paper presentation Mathieu Nassif: Designing Engaging Games Using Bayesian Optimization Olamilekan Fadahunsi: Atelier: Repurposing Expert Crowdsourcing Tasks as Micro-internships Richard Olaniyan: Pay It Backward: Per-Task Payments on Crowdsourcing Platforms Reduce Productivity Akash Singh: Crowdsourcing Annotations for Websites’ Privacy Policies: Can It Really Work?
Lecture 15	Oct 27	Project Discussion
Lecture 16	Nov 1	Student platform presentation Mathieu Nassif: Galazy Zoo Rudolf Lam: Malaria Spot
Lecture 17	Nov 3	Student platform presentation Patrick Sejour Chassagne: Eyewire Richboy Echomgbe: Duolingo
Lecture 18	Nov 8	Student plateform presentation Olamilekan Fadahunsi: Folding@Home Richard Olaniyan: Foldit
Lecture 19	Nov 10	Student platform presentation Akash Singh: Turkomatic Mansha Imtiyaz: Tomnod Faizy Ahsan: Crowdflower
Lecture 20	Nov 15	Invited talk: Edward Newell, McGill University	[Slides]
Lecture 21	Nov 17	Swarm computing	[Slides]
Lecture 20	Nov 22	Restructuring Expert Attention
Lecture 22	Nov 24	Patterns of Online Collaboration
Lecture 23	Nov 29	The challenges of open science
Lecture 24	Dec 1	Limits and the Potential of Collective Intelligence

Note: This schedule is subject to modification.

Background & Pre-requisites
Good understanding of basic algorithms and artificial intelligence concepts. Basic progamming in Python.
Undergraduate students: You can register to this class with the permission of the instructor.

Policy on collaborations
We greatly encourage you to discuss the assignment problems with each other. However, these discussions should not go so far that you are sharing code or giving away the answer. A rule of thumb is that your discussions should considered public in the sense that anything you share with a friend should be sharable with any student in the class. We ask you to indicate on your assignments the names of the persons with who you collaborated or discussed your assignments (including TA’s and instructors).

Policy on final grades
I will use the same rules and formula for calculating the final grade for everyone. We understand that your performances may be influenced by many factors, possibly out of your control. However, that is the only way we can be fair. The only exceptions will be medical exceptions. In that case, I will require a medical note, which has to be also reported to McGill, and to be informed as early as possible. Failure to comply to these rules, may results in the impossibility to invoke a medical exception.

Policy on written reports
Due date/time, location/mode for returning your solutions, and accepted formats will be announced in class and indicated on the course web page.
Failure to return your assignment in time will results in penalties or even absence of grading.
Importantly, solutions that do not follow the requested format will receive a penalty. By default, we only accept PDF or TEXT files. Images (if any) must be embedded in a PDF. Do not compress your files. All files must open on LINUX SOCS workstations.
The quality of the presentation of your solution is very important. Unreadable material, cryptic notations, or bad organization of the material will results in penalties, and potentialy even an absence of grading. If you scan your hand-written solutions, it is your responsability to ensure that you submit a high-quality image (i.e. excellent luminosity, contrast, focus and resolution). The clarity of your explanations will also be an integral part of your final grade.

Policy on programming code
Questions in assignments may require you to write a Python program. We will provide, as much as possible, input and output data to test your programs. However, it will be your duty to ensure that your files compile on LINUX SOCS workstations. We will not grade programs that do not compile on these machines.
Submission of class files (instead of Java source files) will be considered as an absence of submission. Do not compress your files.

Use of French in assignments and exams
In accordance with McGill University’s Charter of Students’ Rights, students in this course have the right to submit in English or in French any written work that is to be graded.

McGill policies
McGill University values academic integrity. Therefore, all students must understand the meaning and consequences of cheating, plagiarism and other academic offenses under the Code of Student Conduct and Disciplinary Procedures. See this link for more information.