Syllabus Computer Engineering MA, Machine Learning, 6 credits

General data

  • Code: DT062A
  • Subject/Main field: Datateknik
  • Cycle: Second cycle
  • Credits: 6
  • Progressive specialization: A1F - Second cycle, has second-cycle course/s as entry requirements
  • Answerable department: Information Systems and Technology
  • Answerable faculty: Faculty of Science, Technology and Media
  • Established: 4/1/2019
  • Date of change: 6/1/2020
  • Version valid from: 7/1/2020


The student should understanding modern machine learning techniques. The student should develop skills in finding interesting features, building graphic and deep learning models by using Python. The student should show an ability to apply the skills in a small project in an real-world business or engineering application area.

Course objectives

Upon completion of the course the student should be able to:
- show a basic understanding of ensemble methods, graphic models and deep learning,
- apply these techniques in a real-world business or engineering application area,
- implement several types of machine learning methods and modify them,
- critically evaluate the methods’ applicability in new contexts.


• Ensemble methods
• Multilayer perceptron
• Convolutional neural network
• Recurrent neural network
• Deep Learning with Python
• Graphic models
• Project

Entry requirements

Computer Engineering BA (AB), including Databases, Modeling and Implementation, 6 credits. Computer Engineering MA, Data Mining, 6 credits. Mathematics BA (A), 30 credits, including Mathematical Statistics, 6 credits.
Total previous studies 120 credits.

Selection rules and procedures

The selection process is in accordance with the Higher Education Ordinance and the local order of admission.

Teaching form

The course may be offered as a campus course or as a web-based distance course. The student time commitment is estimated to about 160 hours.

Examination form

L101: Laboratory exercise, 1.0 hp
Grade scale: Fail (U) or Pass (G)

P101: Project with written report, 2.0 hp
Grade scale: Fail (U) or Pass (G)

T101: Written Exam, 3.0 hp
Grade scale: Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

The final grade is based on combined exam and project assessment.

Grading criteria for the subject can be found at

The examiner has the right to offer alternative examination arrangements to students who have been granted the right to special support by Mid Sweden University’s disabilities adviser.

If examination on campus cannot be conducted according to decision by the vice-chancellor, or whom he delegated the right to, the following applies: Written Exam T101, will be replaced with two parts, online examination and follow-up. Within three weeks of the online examination, a selection of students will be contacted and asked questions regarding the examination. The follow-up consists of questions concerning the execution of the on-line exam and the answers that the student have submitted.

Grading system

Seven-grade scale, A, B, C, D, E, Fx and F. Fx and F represent fail levels.

Course reading

Reference literature

  • Author: Christopher Bishop
  • Title: Pattern recognition and Machine Learning
  • Publisher: Springer
  • Edition: 2006

Required literature

  • Author: Witten, Frank, Hall
  • Title: Datamining - Pratical Machine Learning Tolls and Techinques
  • Publisher: Elsivier
  • Edition: Third edition 2011 or later