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Blekinge Institute of Technology
Department of Mathematics and Natural Science

Revision: 3
Reg.no: BTH-4.1.14-0624-2026


Course syllabus

Robust Methods

Robust Methods

6 credits (6 högskolepoäng)

Course code: MS1415
Main field of study: The course is not included in any main field of study at BTH
Subject: Mathematics statistics
Disciplinary domain: Natural sciences
Education level: First-cycle
Specialization: G1F - First cycle, has less than 60 credits in first-cycle course/s as entry requirements

Language of instruction: English
Applies from: 2027-01-18
Approved: 2026-10-20

1. Decision

This course is established by Dean 2015-05-23. The course syllabus is approved by Head of Department of Mathematics and Natural Science 2026-10-20 and applies from 2027-01-18.

2. Prior knowledge

2.1 Entry requirements

Admission to the course requires completed course in Mathematical Statistics 6 credits.

3. Objective and content

3.1 Objective

The aim of this course is to provide the student with theoretical as well as practical knowledge in mathematical and statistical modelling of phenomena e.g. waves at sea, demand for electric power, sensor and radar signals or stock market option prices. The course provides a basis for further studies in e.g. cybernetics, biorobotics, artificial intelligence (AI), machine learning, financial mathematics and time series analysis.

3.2 Content

The course covers the following parts:

  • Understand, analyze and develop mathematical and statistical models that are the fundamental components of modern machine learning methods.
  • Theoretical knowledge regarding robust methods for:
    - estimation and validation
    - prediction and interpolation
    - discrete dynamic stochastic systems
    - essentially linear systems.
  • Something about nonlinear systems .
  • Time series analysis.
  • Modelling of time-variable stochastic phenomena.
  • Choice of model structure - physical process, observed data.
  • A few different models' properties and predictive ability.
  • Estimation of model parameters.
  • Complexity, performance and measurement errors.
  • Statistical models and methods in time series analysis.
  • Orientation on robust methods and detection of anomalous values.
  • ARMA processes, MK and ML methods, and recursive and adaptive variants.
  • Gaussian and non-gaussian based models.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completion of the course, the student will be able to:

  • master basic robust methods and their application in engineering subjects, engineering, computer science, and economics.
  • master the statistical principles of the robust methods for estimation and validation, prediction and interpolation of linear systems.
  • be familiar with the most important English terms in the field of a foundation for further study in, e.g. financial statistics and nonlinear time series.

4.2. Competence and skills

On completion of the course, the student will be able to:

  • have knowledge of how to practically go about collecting data material, and e.g. procedures to be able to apply the knowledge.
  • discuss applications for robust systems and relate to scientific research problems.
  • design, implement and test different robust methods in some context.
  • evaluate robust methodologies, e.g. for some machine learning method.

4.3. Judgement and approach

On completion of the course, the student will be able to:

  • have the ability to draw conclusions from statistical material.
  • have the ability to propose an appropriate robust method and approach in problem solving.
  • have the ability to critically review others' choice of methods and what conclusions can be drawn.
  • plan and carry out experiments to evaluate and compare methods for example for machine learning.
  • choose a robust method and analyze its performance.

5. Learning activities

The course is given in the form of lectures and exercises as well as an associated laboratory session. Teaching is conducted in English.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2205 Written examination[1] 4.5 credits AF
2215 Laboratory session 1.5 credits GU

[1] Determines the final grade for the course, which will only be issued when all components have been approved.

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

An Introduction to Time Series Modeling - av Andreas Jakobsson (ISBN 9789144134031) 2020 senaste upplagan, Studentlitteratur

Hyndman, R.J. and Athanasopoulos, G., 2018. "Forecasting: Principles and practice." OTexts.