Tilastot, todennäköisyys ja optimointi (5 op)

Arviointiasteikko

H-5

Tavoitteet

The student learns fundamental mathematical concepts, principles, tools (including computing environments) and terminology of statistics, probability and optimization for professional studies.

Sisältö

Theory of statistics and probability
- Numerical and graphical description of data
- Probability, probability rules and theorems, probability distribution
- Modeling, parameter estimation
- Model selection, decision theory
- Analysis task types

Optimization, differential calculus and numerical computation
- Objective function
- Critical points, extrema
- Types of optimization problems
- Limit, derivative, partial derivative, differentiation rules
- Iterative gradient-based optimization methods, derivative-free methods

Aika ja paikka

Spring term 2024, Lapland University of Applied Sciences, Rantavitikka campus (Rovaniemi, Jokiväylä 11)

Oppimateriaalit

Study material is available as an eBook and on the Moodle learning platform.

Opetusmenetelmät

Lessons and exercises

Tenttien ajankohdat ja uusintamahdollisuudet

The number and date of exams will be agreed on during the course. Resit is possible by the end of the next term.

Toteutuksen valinnaiset suoritustavat

Studying independently is possible. All exercises must be returned in time to be evaluated.

Opiskelijan ajankäyttö ja kuormitus

5 credits, approximately 135 hours of work

Arviointikriteerit, tyydyttävä (1)

Assessment criteria - grade 1
The student knows the concepts of probability, statistics, and optimization and is able to solve basic problems.

Arviointikriteerit, hyvä (3)

Assessment criteria - grade 3
The student understands the concepts of probability, statistics, and optimization and is able to solve varied problems related to applications of probability, statistics, and optimization.

Arviointikriteerit, kiitettävä (5)

The student understands the concepts of probability, statistics, and optimization and is able to apply methods of probability, statistics, and optimization in solving and handling new types of problems.