Course Syllabus for

Machine Learning

FMA085F, 5 credits

Valid from: Autumn 2012
Decided by: FN1/Anders Gustafsson
Date of establishment: 2013-03-24

General Information

Division: Mathematics
Course type: Third-cycle course
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, C. M.: Pattern Recognition and Machine Learning. Springer, 2006. ISBN 9780387310732.

Instruction Details

Type of instruction: Lectures

Examination Details

Examination formats: Written exam, written assignments. Take-home exam
Grading scale: Failed, pass

Admission Details

Assumed prior knowledge: Linear algebra, calculus in several variables, linear systems, and probability theory.

Further Information

The course is given by professor Cristian Sminchisescu.

Course Occasion Information

Contact and Other Information

Course coordinators:

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