MEST5904 Masteroppgave Mote og samfunn Emneplan

Masteroppgaven er et større selvstendig arbeid som utforsker en spesifikk problemstilling med vekt på mote og motens rolle i samfunnet. Masteroppgaven skal bidra til å problematisere, drøfte og reflektere over mote i et samfunns- og fremtidsperspektiv. Aktuell tematikk kan være: sosiale, politiske, økonomiske og industrielle system, knyttet til mote og bærekraft. Masteroppgaven gjennomføres som et avgrenset forskningsarbeid, med både en skriftlig og en praktisk komponent. Sentrale elementer som problemstilling, teori, metode, undersøkelse og drøfting inngår.

Studenten velger mellom to ulike eksamensformer. Eksamensform A vektlegger praktisk eksamen og eksamensløsning B vektlegger skriftlig oppgave. Se utdypning under «Vurdering/eksamen». Emnet begynner i 3. semester, etter MEST4800, og har eksamen i 4. semester.Retningslinjer for masteroppgaver ved fakultetet finner du her: Retningslinjer for masteroppgaver ved Fakultet for teknologi, kunst og design - Student - minside (oslomet.no)

None

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and general competence.

Knowledge

The student can:

• explain fundamental concepts in regression analysis such as explanatory variables, response variable, explained variance, prediction intervals and the least squares method
• know of maximum likelihood estimation, likelihood ratio test, bootstrapping and generalized linear models
• explain how machine learning methods are trained, validated, and evaluated
• explain the difference between local and global explanations, and how the explainability methods partial dependence plot, accumulated local effects plot, Friedman's H-statistic, and surrogate models work.

Skills

The student can:

• use linear and logistic regression to study the relationship between one or more explanatory variables and a response variable
• fit an appropriate regression model by studying the model's residuals and, if necessary, attempt various variable transformations and variable selection
• formulate and perform hypotheses tests and calculate confidence intervals for parameters in a regression model
• identify the most important predictors in a model using explainable artificial intelligence (XAI)
• write source code to perform calculations for regression analyses and XAI in Python

General competence

The student can:

• apply statistical thinking to real-world problems and communicate these both in writing and orally
• solve real-world problems using regression analysis, machine learning and XAI
• identify the limitations of the methods and assess similarities and differences with methods from mathematical modeling, numerical analysis, and machine learning

Lectures and individual exercises. The exercise sessions will consist of both problem-solving and programming.

The following coursework is mandatory and must be approved to be eligible for the exam:

Three mandatory assignments, of which at least two must be approved. The work is done in groups of 1-4 members.

Individual written exam of 3 hours under supervision.

The exam result can be appealed.

In the event of resit or rescheduled exams, another exam form may also be used. If oral exams are used, the result cannot be appealed.

Grade scale A-F

One internal examiner. External examiners are used regularly.

Kristoffer H. Hellton

The course builds on DAPE2000 Mathematics 2000 with Statistics.

Ingen overlapp med andre emner.