|Type of Instruction
||14 x 2 hours lectures;14 online contact hours
|Type of exams
||written exam (60%) and group assignments (40%)
|Course load:||6 ECTS credits
|Registration:||This course has a maximum capacity of 40 participants. If you filled out the survey you received recently (“selecting master courses CIS Fall semester 2017) you will be enrolled automatically. Other students: see specifics.
|Blackboard Info||Link to
Blackboard (When you see 'Guest are not allowed in this course', please login at Blackboard itself)
At the end of the unit, students will be able to
- describe the idea of wisdom of the crowd as used in collective intelligence systems;
- apply crowdsourcing collection systems to particular problems;
- list a range of collective intelligence applications;
- describe the internals of components that are used in collective intelligence applications;
- analyze, break down and design complex collective intelligence end-user systems from simple components.
The Internet allows for information to be shared all over the world and anyone with an Internet connection can access this information whenever they want. Nowadays people not only consume information, they also collectively produce and share information. For examples, every day, millions of people write tweets about things that interest them, rate songs they listen to, post videos on YouTube and add information to the world wide web in a myriad of other ways.
This collectively produced data, often called Web 3.0, can be the source of information useful for companies and researchers alike. In this course we will examine the idea of social intelligence and wisdom of the crowd. Additionally, we can simulate the behavior underlying collective intelligence, including alternative forms of collective behavior, such as ant colony optimization. Some of the topics we will discuss are sentiment analysis, recommender systems and crowdsourcing.
Questions that arise from this are, for instance: What are the components needed to determine the sentiment of a tweet? How do we recommend a song to a Spotify user or a book to an Amazon client? How do we extract relevant information? Can we effectively use collective behavior to identify (near) optimal solutions to find the best path in a complex environment? How do we evaluate these methods?
This course has a maximum capacity of 40 participants. If you filled out the survey you received recently (selecting master courses CIS Fall semester 2017) you will be enrolled automatically. All other students have to send an e-mail to: mastercoursesCIW@uvt.nl and will be enrolled if places are available.
The final grade of the course consists of a weighted average of the written exam (60%) and group assignments (40%). This means that it is not required for the assignment or the written exam to be a pass, only the final grade (weighted average) should be a pass in order to pass the course. For the written exam a resit is possible. For the assignments revisions (resits) are not allowed. Additional compulsory assignments require a pass in order to pass for the course.
Assignments have non-negotiable deadlines. Assignments handed in after the deadline will not be accepted and will lead to a fail for the course.
- Online articles and slides.
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