Course syllabus

Machine Learning

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

Valid for: 2016/17
Decided by: Education Board B
Date of Decision: 2016-03-29

General Information

Elective for: BME4, D4-bg, D4-pv, E4, F4, Pi4
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

demonstrate 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
Assessment: Compulsory assignments including computer work and written reports. Approved results on these are enough to pass the course. To get a higher grade, a written and an oral test are required.


Required prior knowledge: FMA420 Linear Algebra, FMA430 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. FMS012.
The number of participants is limited to: No

Reading list

Contact and other information

Course coordinator: Cristian Sminchisescu,
Director of studies: Anders Holst,
Course homepage: