Syllabus for |
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MVE440 - Statistical learning for big data |
Statistik för stora datamängder |
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Syllabus adopted 2015-02-11 by Head of Programme (or corresponding) |
Owner: MPENM |
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7,5 Credits
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Grading: TH - Five, Four, Three, Fail |
Education cycle: Second-cycle |
Major subject: Mathematics
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Department: 11 - MATHEMATICAL SCIENCES
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The course is full. For waiting list, please contact the director of studies: norbeck@chalmers.se
Teaching language: English
Application code: 20130 Open for exchange students: Yes
Minimum participants: 3 Maximum participants: 120
Module |
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Credit distribution |
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Examination dates |
Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
0115 |
Examination |
7,5 c |
Grading: TH |
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7,5 c
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Contact examiner, |
Contact examiner, |
Contact examiner |
In programs
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 2 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
Examiner:
Rebecka Jörnsten
Go to Course Homepage
Eligibility: In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement:
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.
Course specific prerequisites
The prerequisites for the course are a basic course in statistical inference and MVE190 Linear Statistical Models. Students can also contact the course instructor for permission to take the course.
Aim
The course should give understanding of and training in techniques for statistical analysis of large data sets.
Learning outcomes (after completion of the course the student should be able to)
- demonstrate
understanding of the key concepts and ideas concerning
classification, clustering and dimension reduction. - solve high-dimensional data analysis exercises and interpret the results of such analyses.
Content
Organisation
The teaching is organized with lectures, discussions, and reading assignments.
Literature
To be announced.
Examination including compulsory elements
Oral and/or written examination.
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