Third-Cycle Courses

Faculty of Engineering | Lund University

Details for Course FMA085F Machine Learning

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  • FMA085F
  • Temporary
Course Name
  • Machine Learning
Course Extent
  • 5
Type of Instruction
  • Third-cycle course
Administrative Information
  • 7151 (Centre of Mathematical Sciences / Mathematics)
  •  -03-24
  • FN1/Anders Gustafsson

Current Established Course Syllabus

  • English
  • If sufficient demand
  • 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.
  • * 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.
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
Examination Formats
  • Written exam
  • Written assignments
  • Take-home exam
  • Failed, pass
Admission Requirements
Assumed Prior Knowledge
  • Linear algebra, calculus in several variables, linear systems, and probability theory.
Selection Criteria
  • Bishop, C. M.: Pattern Recognition and Machine Learning. Springer, 2006. ISBN 9780387310732.
Further Information
  • The course is given by professor Cristian Sminchisescu.
Course code
  • FMA085F
Administrative Information
  •  -03-24
  • FN1/Anders Gustafsson

All Established Course Syllabi

1 course syllabus.

Valid from First hand in Second hand in Established
Autumn 2012 2012‑12‑21 11:22:21 2013‑01‑17 16:30:24 2013‑03‑24

Current or Upcoming Published Course Occasion

No matching course occasion was found.

All Published Course Occasions

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0 course occasions.

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