Valid for: 2023/24
Faculty: Faculty of Engineering, LTH
Decided by: PLED I
Date of Decision: 2023-04-14
Main field: Technology.
Elective for: F4, Pi4, R4
Language of instruction: The course will be given in English
The course begins with an overview of basic data wrangling and visualisation. With a focus on the student's ability to identify and illustrate important features of the data.
Then important methods in statistical learning are introduced. Emphasis is given to dimension reduction, supervised and unsupervised learning. Issues arising from fitting multiple models (i.e. multiple testing) as well as the methods relationship to regression are discussed. Computer based labs and projects form an imporant part of the learning activities. The course concludes with a project where the students will select suitable methods to analyze a given data material.
Knowledge and understanding
For a passing grade the student must
Competences and skills
For a passing grade the student must
Judgement and approach
For a passing grade the student must
Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: The final grade is determined by the final project.
The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.
Parts
Code: 0123. Name: Computer Lab 1.
Credits: 2. Grading scale: UG. Assessment: Written report. Contents: Data handling and visualisation.
Code: 0223. Name: Computer Lab 2.
Credits: 2. Grading scale: UG. Assessment: Written report. Contents: Supervised learning.
Code: 0323. Name: Computer Lab 3.
Credits: 2. Grading scale: UG. Assessment: Written report. Contents: Unsupervised learning.
Code: 0423. Name: Project.
Credits: 1,5. Grading scale: TH. Assessment: Written project report Contents: Final project
Assumed prior knowledge: A basic course in mathematical statistics and knowledge in linear algebra.
The number of participants is limited to: 50
Selection: Completed university credits within the program. (Note that only credits which according to Ladok have been included in the program before the selection process count. For students taking master's programmes 180 credits corresponding to a bachelor's degree are added.) Priority is given to students enrolled on programmes that include the course in their curriculum. Among these students place is guaranteed to those in the specialisation on Riskmodellering at Risk, säkerhet och krishantering education.
The course overlaps following course/s: FMSF86, FMAN45, EDAN96
Director of studies: Johan Lindström, studierektor@matstat.lu.se
Course coordinator: Linda Hartman, linda.hartman@matstat.lu.se
Course homepage: https://www.maths.lu.se/utbildning/civilingenjoersutbildning/matematisk-statistik-paa-civilingenjoersprogram/
Further information: Given in parallell with FMSF86. Only one of the courses FMSF86 and FMSF90 may be included in a degree. The course overlaps with EDAN96.