Syllabus for |
|
MVE441 - Statistical learning for big data |
Statistik för stora datamängder |
|
Syllabus adopted 2020-02-05 by Head of Programme (or corresponding) |
Owner: MPENM |
|
7,5 Credits
|
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
Education cycle: Second-cycle |
Major subject: Mathematics
|
Department: 11 - MATHEMATICAL SCIENCES
|
Teaching language: English
Application code: 20150 Open for exchange students: Yes
Module |
|
Credit distribution |
|
Examination dates |
Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
0120 |
Project |
1,5 c |
Grading: UG |
|
|
|
|
1,5 c
|
|
|
|
|
0220 |
Take-home examination |
6,0 c |
Grading: TH |
|
|
|
|
6,0 c
|
|
|
|
|
In programs
MPENM ENGINEERING MATHEMATICS AND COMPUTATIONAL SCIENCE, MSC PROGR, Year 1 (compulsory elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 1 (compulsory elective)
MPCAS COMPLEX ADAPTIVE SYSTEMS, MSC PROGR, Year 2 (elective)
Examiner:
Rebecka Jörnsten
Go to Course Homepage
Eligibility
General entry requirements for Master's level (second cycle) Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level) Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
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.
|
|