Course syllabus

Tillämpad maskininlärning
Applied Machine Learning

EDAN95, 7,5 credits, A (Second Cycle)

Valid for: 2020/21
Decided by: PLED C/D
Date of Decision: 2020-03-30

General Information

Elective for: BME4, C4-pv, D4-bg, D4-mai, E4-bg, F4, F4-pv, F4-mai, MSOC2, Pi4-fm, Pi4-pv, MMSR2
Language of instruction: The course will be given in English


To give an introduction to several subdomains of machine learning and to give an orientation about fundamental methods and algorithms within these domains. To convey knowledge about breadth and depth of the domain.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

Competences and skills
For a passing grade the student must


Judgement and approach
For a passing grade the student must





Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: (Laboratory) Assignments and optional written exam. Completed assignments result in a pass (mark 3), better grades can be achieved through participation in the optional written exam.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.


Admission requirements:

The number of participants is limited to: 60
Selection: Completed university credits within the program. Priority is given to students enrolled on programmes that include the course in their curriculum.

Reading list

Contact and other information

Teacher: Pierre Nugues,
Course coordinator: Elin Anna Topp,
Teacher: Volker Krueger,
Course homepage:
Further information: Detailed rules concerning the assignments will be found in the course web site.