Syllabus for Statistical Machine Learning

Statistisk maskininlärning


  • 5 credits
  • Course code: 1RT700
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Technology A1N, Image Analysis and Machine Learning A1N, Mathematics A1N, Computer Science A1N, Data Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2016-03-08
  • Established by:
  • Revised: 2023-02-08
  • Revised by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2023
  • Entry requirements:

    120 credits including Probability and Statistics, Linear Algebra II, Single Variable Calculus and a course in introductory programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.

  • Responsible department: Department of Information Technology

Learning outcomes

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

  • discuss and determine if a technical problem described without specialized terminology can be formulated as a supervised machine learning problem and, if so, make this formulation.
  • structure and divide supervised machine learning problems into tractable sub-problems, formulate a mathematical solution to the problems and implement this solution using statistical software.
  • use and develop supervised machine learning models for classification and regression problems.
  • describe the assumptions underlying supervised machine learning and the limitations that follow from these, including potential consequences for ethics and sustainability.
  • analyse the quality of a model and use cross-validation for model selection and model tuning.
  • explain important principles for generalization, including the trade-off between bias and variance, overfitting and underfitting.
  • critically examine and provide constructive criticism on other students' reports about machine learning.


This is an introductory course in statistical machine learning, focusing on classification and regression: linear regression, regularization, logistic regression, discriminant analysis, classification and regression trees, ensemble methods, neural networks, deep learning; practical considerations such as cross validation, model selection, the bias-variance trade off, applying the methods to real data; ethical and sustainability considerations when using statistical machine learning.


Lectures, problem solving sessions (both with and without computer), laboratory work, feedback on written assignments including a minor project.


Written exam combined with oral and written presentation of assignments.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Reading list

Reading list

Applies from: Autumn 2023

Some titles may be available electronically through the University library.

  • Machine learning : a first course for engineers and scientists Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas

    Cambridge: Cambridge University Press, 2022

    Find in the library