Course Syllabus for

Introduction to Machine Learning
Introduktion till maskinlärning

FMAN45F, 7.5 credits

Valid from: Autumn 2016
Decided by: Professor Thomas Johansson
Date of establishment: 2017-02-09

General Information

Division: Mathematics
Course type: Course given jointly for second and third cycle
The course is also given at second-cycle level with course code: FMAN45
Teaching language: 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 doctoral student must

Competences and Skills

For a passing grade the doctoral student must

Judgement and Approach

For a passing grade the doctoral student must demonstrate the ability to critically evaluate and compare different learning models and learning algorithms for different problem setups and quality characteristics.

Course Contents

* Training, testing, generalization, hypothesis spaces. *Linear regression and classification. *Kernel methods and support vector machines. *Graphical models. *Mixture models, Expectation Maximization. *Variational and sampling methods

Course Literature

Bishop, Christopher M.: Pattern Recognition and Machine Learning. Springer Verlag, 2006. ISBN 9780387310732.

Instruction Details

Types of instruction: Lectures, miscellaneous. Scheduled resource time during which the participants can get assistance with the assignments.

Examination Details

Examination format: Written assignments
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: FMAF05 Mathematics - Systems and transforms and FMS012/FMSF45 Mathematical statistics, basic course.
Selection criteria: Credits awarded in the courses FMS012, FMSF20, FMSF10, FMSN40, FMA051, FMA120 and FMAN20.

Course Occasion Information

Contact and Other Information

Course coordinators:
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