Valid for: 2017/18
Decided by: PLED F/Pi
Date of Decision: 2017-04-06
Elective for: BME4, D4-bg, D4-pv, E5, F4, F4-bg, F4-fm, 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.
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.
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.
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: 60
Selection: Credits awarded in the courses FMSF45, FMSF20, FMSF10, FMSN40, FMAN60, FMAN70 and FMAN20.
Teacher: Cristian Sminchisescu, cristian.sminchisescu@math.lth.se
Course coordinator: Anders Holst, studierektor@math.lth.se
Course administrator: Studerandeexpeditionen, expedition@math.lth.se
Course homepage: http://www.ctr.maths.lu.se/course/machinlearn/