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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
MVE051 - Mathematical statistics and discrete mathematics  
Matematisk statistik och diskret matematik
 
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: TKITE
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: First-cycle
Major subject: Mathematics
Department: 11 - MATHEMATICAL SCIENCES


Teaching language: Swedish
Application code: 52131
Open for exchange students: No
Only students with the course round in the programme plan

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0113 Written and oral assignments 1,5 c Grading: UG   1,5 c    
0213 Examination 6,0 c Grading: TH   6,0 c   04 Jun 2020 pm J,  07 Jan 2020 pm SB_MU   26 Aug 2020 pm J

In programs

TKITE SOFTWARE ENGINEERING, Year 2 (compulsory)

Examiner:

Nancy Abdallah

  Go to Course Homepage

Replaces

MVE050   Mathematical statistics and discrete mathematics


Eligibility:

In order to be eligible for a first cycle course the applicant needs to fulfil the general and specific entry requirements of the programme(s) that has the course included in the study programme.

Course specific prerequisites

Basic knowledge of discrete mathematics, linear algebra and calculus.

Aim

The aim of the course is to give
- understanding of basic knowledge in probability theory, statistics, and combinatorics which is important for technical studies and specifically for studies in information technology
- skills for understanding and using mathematical language
- ability to communicate mathematics

Learning outcomes (after completion of the course the student should be able to)

- identify problems arising in technical studies and specifically in information technology for which the treatment requires use of fundamental concepts and methods from Probablity theory and Mathematical statistics.
- describe and analyze such problems in terms of statistics and discrete mathematics.
- apply basic statistical mehods such as parameter and interval estimation, testing of statistical hypotheses, and linear regression, in problem solving.

Content

The course covers topics in a number of areas. Within each area relevant mathematical concepts are studied. These concepts are considered on different levels of depth. The topics discussed are:
- Probability theory and Markov chains: random variables, expectation, variance, correlation, conditional probability, the law of large numbers, the central limit theorem.
- Statistics: point estimation, confidence intervals, hypotheses testing.
- Combinatorics: combinations, permutations, generating functions.

In Probability theory, the emphasis is on discrete models.

Organisation

The teaching is built up around certain themes. The mathematical concepts involved are first outlined and then studied more deeply within the framework of the following course activities:
- Lectures which elucidate and explain the mathematical theory
- Exercise sessions where related problems are solved individually or in groups.

Literature

To be announced.

Examination including compulsory elements

Written examination. Compulsory turn in assignments.


Page manager Published: Thu 04 Feb 2021.