Web Science (WS)

Course content

The course objective is to offer an advanced introduction into Web Science. The goal is to understand and model the Web as a structure and to design and evaluate some of the major technologies operating on the Web (see below). Through applied projects, the course aims to stimulate and prepare students for their MSc thesis work.

 

Content in detail:

  • The World Wide Web as a network and its challenges
  • Recommender systems
  • Collective intelligence and crowdsourcing
  • Opinion and data mining
  • Data analytics
Education

MSc Programme in Computer Science

Learning outcome

Knowledge

  • The basic models and techniques of mining information on the Web
  • Different criteria for analytics applications

 

Skills

Students should be able to transfer the above knowledge to real-world tasks by:

  • Designing appropriate strategies for crawling, mining and analysing Web information
  • Planning and carrying out appropriate evaluations
  • Diagnosing problems in standard Web mining and analytics applications
  • Designing and calibrating solutions appropriate for expected usage loads

 

Competences

  • Explain basic Web principles and properties to both laymen and specialists
  • Use standard procedures and practices when designing or implementing Web mining and analytics solutions
  • Present evaluation analyses and results so that a technically qualified person can follow and obtain similar findings

The course will use a combination of lectures, lab sessions, class discussions and student presentations. Where possible, relevant guest lecturers will be involved.

 

The literature consists of seminal research and review articles from central journals and selected papers from peer-reviewed conferences, textbooks and research reports. This is supplemented with practical experience gained through lab sessions.

See Absalon for a list of literature.

It is expected that students know how to program and have a working knowledge of Machine Learning that can be obtained by any undergraduate course in Machine Learning.

Academic qualifications equivalent to a BSc degree is recommended.

Written
Individual
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Written assignment
Oral defence
The exam consists of two parts:

1. A written individual report
2. 20 minutes oral examination without preparation

The grade will be based on an overall assessment.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.
Criteria for exam assessment

See Learning Outcome.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Preparation
  • 14
  • Theory exercises
  • 57
  • Practical exercises
  • 57
  • Project work
  • 50
  • English
  • 206