Course syllabus

Machine Learning

FMAN45, 7,5 credits, A (Second Cycle)

Valid for: 2018/19
Decided by: PLED F/Pi
Date of Decision: 2018-03-23

General Information

Elective for: BME4, D4-bg, D4-pv, E5, F4, F4-bs, F4-bg, F4-fm, F4-r, I4, Pi4-bg
Language of instruction: The course will be given in English


To give knowledge about the basic theory for Machine Learning -- construction of automatised systems that can learn/gather information from data, for example learn to recognize characters in a hand-written text.

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

have demonstrated the ability to critically evaluate and compare different learning models and learning algorithms for different problem setups and quality characteristics.



Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Compulsory assignments including computer work and written reports. Written examination. For those who do not get all the assignments approved during the course there will be a chance to hand in improved versions during the following semester.

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:

Required prior knowledge: FMAB20 Linear Algebra, FMAB30 Calculus in Several Variables, Fourier analysis and theory of linear systems corresponding to FMAF05 Mathematics-Systems and Transforms, and one of the basic courses in Mathematical Statistics, e.g. FMSF45.
The number of participants is limited to: 80
Selection: Credits awarded in the courses FMSF45, FMSF20, FMSF10, FMSN40, FMAN20, FMAN60 and FMAN70.

Reading list

Contact and other information

Teacher: Cristian Sminchisescu,
Course coordinator: Anders Holst,
Course administrator: Studerandeexpeditionen,
Course homepage: