From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence.
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851-0585-45L  Machine Learning and Modelling for Social Networks

SemesterSpring Semester 2017
LecturersO. Woolley, N. Antulov-Fantulin, I. Moise, L. Sanders
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 50.



Catalogue data

AbstractThis mini-course covers computational and statistical methods to characterize the structure and dynamics of complex social networks. We cover methods such as clustering, classification, spectral analysis and Montecarlo and also specific applications to social network data and spreading processes on these networks. We discuss current research and ethical questions raised by applications.
ObjectiveThis advanced course will give students insight into the questions that can be answered analyzing network data and into the related challenges. They will be exposed to the main methods that can be used to tackle these questions and learn about the shortcomings of these current methods. We will also raise students awareness of some of the ethical questions raised, mainly in the realm of privacy, by the types of data collected and the influence on individual behavior that can be achieved through technologies built on the methods presented in class. Students will be encouraged to apply their knowledge to a specific network dataset by producing a research proposal.
Prerequisites / NoticeStudents must be in their 5th semester or more advanced.
Knowledge of basic: linear algebra, differential equations, probability, statistics and programming.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits2 credits
ExaminersO. Woolley, N. Antulov-Fantulin, I. Moise, L. Sanders
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition possible without re-enrolling for the course unit.
Admission requirementStudents must be in their 5th semester or more advanced.
Knowledge of basic: linear algebra, differential equations, probability, statistics and programming.
Additional information on mode of examinationGrades will be based on contribution to discussion and on a research proposal applying the tools and/or addressing questions discussed in the course.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
851-0585-45 VMachine Learning and Modelling for Social Networks
Dates: 8.5. - 12.5.2017, 9-12
15s hrs
08.05.09-12HG E 3 »
09.05.09-12ML H 37.1 »
10.05.09-12ML H 37.1 »
11.05.09-12LEE E 101 »
12.05.09-12LEE E 101 »
O. Woolley, N. Antulov-Fantulin, I. Moise, L. Sanders

Groups

No information on groups available.

Restrictions

Places50 at the most
Waiting listuntil 12.02.2017

Offered in

ProgrammeSectionType
GESS Science in PerspectiveSociologyWInformation