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Third-Cycle Courses

Faculty of Engineering | Lund University

Details for the Course Syllabus for Course FMAN45F valid from Autumn 2016

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General
Aim
  • 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.
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
Knowledge and Understanding
  • For a passing grade the doctoral student must
  • have a good knowledge of the statistical principles used in machine learning

    have knowledge of the disciplinary foundation for the design and analysis of learning algorithms and systems

    demonstrate in-depth knowledge of methods and theories in the field of machine learning.
Competences and Skills
  • For a passing grade the doctoral student must
  • demonstrate abilities to develop learning techniques and systems based on relevant technological issues

    demonstrate the ability to identify, formulate, design, and implement learning components and applications, and write a report on this.
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.
Types of Instruction
  • Lectures
  • Miscellaneous
  • Scheduled resource time during which the participants can get assistance with the assignments.
Examination Formats
  • Written assignments
  • Failed, pass
Admission Requirements
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.
Literature
  • Bishop, Christopher M.: Pattern Recognition and Machine Learning. Springer Verlag, 2006. ISBN 9780387310732.
Further Information
Course code
  • FMAN45F
Administrative Information
  • 2017-02-09
  • Professor Thomas Johansson

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