Eligibility and selection

Master's Programme, Machine Learning, 120 credits (TMAIM), Programme syllabus for studies starting in autumn 2013

Last edit: 14/03/2014
Approved: 14/03/2014

Students from KTH Bachelor’s Programs Leading to Civilingenjör in Combination with the Machine Learning Program

A number of Bachelor’s programmes at KTH give the degree of Civilingenjör in combination with Machine Learning. Students from these programmes are accepted without selection to the Machine Learning programme, provided that they fulfill the KTH requirements for promotion to study year 4 and have completed the courses listed below under specific admission requirements.  Applications must be submitted according to instructions of the CSC school.

Other Students

General admission requirements: See the KTH general admission requirements for Master’s programmes, link below 

Specific admission requirements: The prerequisites for the Master's programme in Machine Learning is a Swedish or foreign degree equivalent to Bachelor’s degree of 180 ECTS credits, with a level in Mathematics and Computer Science equal, or higher, than that of the following courses at KTH: SF1604 Linear algebra (or SF1624), SF1625 Calculus in one variable, SF1626 Calculus in several variables, SF1901 Probability theory and statistics, and DD1341 Introduction to computer science (or DD1320, DD1321, DD1340, DD1344). Applicants must also provide a proof of good knowledge in English.

Selection: The selection process is based on a total evaluation of the following selection criteria: grade point average (GPA), course work related to the programme (e.g, in the fields of Machine Learning, Computer Vision, Image Processing, Speech Processing, Signal Processing, Neuroscience, Information Retrieval, or Data Mining), letter of intent, and references.

Further information

Complete information on the eligibility requirements can be found in the local admission policy of KTH, see 

http://intra.kth.se/regelverk/utbildning-forskning/grundutbildning/antagning/1.276218

and on the KTH Studies web pages: http://www.kth.se/en/studies/programmes/master/admission